CN115932723A

CN115932723A - Positioning method, positioning device, computer equipment, storage medium and program product

Info

Publication number: CN115932723A
Application number: CN202211734902.9A
Authority: CN
Inventors: 李晓东; 齐望东; 刘鹏; 尤肖虎; 黄永明; 刘升恒; 郑旺; 潘孟冠
Original assignee: Network Communication and Security Zijinshan Laboratory
Current assignee: Network Communication and Security Zijinshan Laboratory
Priority date: 2022-12-31
Filing date: 2022-12-31
Publication date: 2023-04-07

Abstract

The application relates to a positioning method, a positioning device, a computer device, a storage medium and a program product, which are used in a target terminal, wherein the method comprises the following steps: receiving uplink signal measurement data sent by a target base station, wherein the uplink signal measurement data comprises AOA data and TOA data; inputting uplink signal measurement data and positioning data of a target terminal at a previous moment into a first positioning state space model, and solving the first positioning state space model based on a preset algorithm to obtain first positioning data of the target terminal at the current moment; the first positioning state space model comprises a first positioning observation model and a first positioning state model, and the first positioning observation model comprises a first incident angle observation model and a first three-dimensional TOA observation model. The method can improve the positioning precision.

Description

Positioning method, positioning device, computer equipment, storage medium and program product

Technical Field

The present application relates to the field of wireless communication technologies, and in particular, to a positioning method, an apparatus, a computer device, a storage medium, and a program product.

Background

For 2D (Two-dimensional) positioning of a terminal, a radio base station measures 1D (One-dimensional) AOA (Angle of arrival) information and TOA (Time of arrival) information of an uplink signal of the terminal. In consideration of the deployment cost of the wireless base station, a single wireless base station is generally adopted to perform 2D positioning on the terminal in a certain area, but in most practical positioning scenes, the space between the wireless base station and the terminal may be blocked by an obstacle, so that uplink signals are subjected to non-line-of-sight propagation, AOA information and TOA information measured by the wireless base station are no longer reliable, thereby causing large positioning errors and even interruption of the positioning process. The use of multiple radio base stations avoids the above-mentioned problems associated with the positioning of a single radio base station, but the cost of deploying multiple radio base stations increases. Therefore, a solution to the problem of accurately positioning the terminal based on the control cost is needed.

In the conventional art, 2D positioning of a terminal is performed based on an AAOM (Azimuth Observation Model) and a 2D TOA Observation Model through 1DAOA information and TOA information measured by a single radio base station.

However, both the AAOM and 2D TOA observation models are constructed based on the assumption that the terminal and the radio base station are located on the same plane, and are only applicable to a case where the terminal is relatively far from the radio base station. When the terminal is closer to the wireless base station, for example, when the terminal is positioned in a 5G micro base station room, the height difference between the terminal and the wireless base station may cause a non-negligible pitch angle, and if the terminal is still positioned in 2D by using the AAOM and 2D TOA observation models according to the assumption, the positioning error may be significantly increased. Therefore, the conventional technology has a problem of low positioning accuracy in the 2D positioning of the near field terminal.

Disclosure of Invention

In view of the foregoing, it is desirable to provide a positioning method, apparatus, computer device, storage medium, and program product capable of improving positioning accuracy in 2D positioning of a far-near field terminal.

In a first aspect, the present application provides a positioning method. The positioning method comprises the following steps: receiving uplink signal measurement data sent by a target base station, wherein the uplink signal measurement data comprises angle of arrival (AOA) data and time of arrival (TOA) data; inputting uplink signal measurement data and first positioning data of a target terminal at the previous moment into a first positioning state space model, and solving the first positioning state space model based on a preset algorithm to obtain the first positioning data of the target terminal at the current moment; the first positioning state space model comprises a first positioning observation model and a first positioning state model, the first positioning observation model comprises a first incident angle observation model and a first three-dimensional TOA observation model, the first incident angle observation model is used for representing the relation between AOA data and first antenna data of a target terminal and second antenna data of a target base station, the first three-dimensional TOA observation model is used for representing the relation between the TOA data and the first antenna data and the second antenna data, and the first positioning state model is used for representing the relation between first positioning data of the current moment of the target terminal and first positioning data of the last moment of the target terminal.

In one embodiment, the first positioning state model is specifically configured to characterize a relationship between the first positioning data of the target terminal at the current time, the first positioning data of the target terminal at the previous time, the state transition matrix of the previous time, the noise input matrix of the previous time, and the state noise vector of the previous time.

In one embodiment, the state transition matrix at the previous time and the noise input matrix at the previous time are obtained from tracking the sampling interval.

In one embodiment, the first antenna data includes antenna position data of the target terminal and the second antenna data includes antenna position data and antenna attitude data of the target base station.

In one embodiment, the antenna position data of the target terminal comprises position coordinates of a phase center of the antenna of the target terminal in a local rectangular coordinate system; the antenna position data of the target base station comprises position coordinates of a phase center of an antenna of the target base station in a local rectangular coordinate system; the antenna attitude data of the target base station includes an attitude angle of the antenna of the target base station in a local rectangular coordinate system.

In one embodiment, the positioning method further comprises: acquiring inertia measurement data of a target terminal; inputting uplink signal measurement data, inertia measurement data and second positioning data of the target terminal at the previous moment into a second positioning state space model, and solving the second positioning state space model based on a preset algorithm to obtain the second positioning data of the target terminal at the current moment; the second positioning state space model comprises a second positioning observation model and a second positioning state model, the second positioning observation model comprises a second incidence angle observation model and a second three-dimensional TOA observation model, the second incidence angle observation model is used for representing the relation between AOA data and first antenna data and the relation between the AOA data and the first antenna data, the second three-dimensional TOA observation model is used for representing the relation between the TOA data and the first antenna data and the relation between the second antenna data, and the second positioning state model is used for representing the relation between second positioning data of the current moment of the target terminal and second positioning data and inertia measurement data of the last moment of the target terminal.

In one embodiment, the positioning method further comprises: constructing a target state equation between the first order differential of the second positioning data of the target terminal at the current moment and the second positioning data and the inertia measurement data of the target terminal at the current moment; and carrying out discretization treatment on the target state equation to obtain a second positioning state model.

In one embodiment, the second positioning data comprises terminal attitude data, terminal speed data and terminal position data, and the target state equation comprises a first state equation, a second state equation and a third state equation; the first state equation is a state equation between first order differential of terminal attitude data of the target terminal at the current moment and the terminal attitude data and inertial measurement data of the target terminal at the current moment; the second state equation is a state equation between first-order differential of the terminal speed data of the target terminal at the current moment and the terminal speed data and inertia measurement data of the target terminal at the current moment; the third state equation is a state equation between the first order differential of the terminal position data of the target terminal at the current time and the terminal position data and the inertial measurement data of the target terminal at the current time.

In one embodiment, the inertial measurement data comprises: white noise of a gyroscope carried on a target terminal on a carrier coordinate system, a rotation matrix between the carrier coordinate system and a navigation coordinate system, white noise of an accelerometer carried on the target terminal on the carrier coordinate system, a projection component of an angular velocity of the carrier coordinate system relative to the navigation coordinate system on the carrier coordinate system, random constant drift of the gyroscope on the carrier coordinate system, specific force output by the accelerometer on the carrier coordinate system and random constant drift of the accelerometer on the carrier coordinate system.

In one embodiment, solving the first positioning state space model based on a preset algorithm to obtain first positioning data of the target terminal at the current time includes: acquiring constraint conditions between the first antenna data and the second antenna data; and solving the first positioning state space model based on a preset algorithm and a constraint condition to obtain the first positioning data of the target terminal at the current moment.

In one embodiment, solving the second positioning state space model based on a preset algorithm to obtain second positioning data of the target terminal at the current time includes: acquiring a constraint condition between the first antenna data and the second antenna data; and solving the second positioning state space model based on a preset algorithm and a constraint condition to obtain second positioning data of the target terminal at the current moment.

In one embodiment, the predetermined algorithm is a particle filter algorithm.

In a second aspect, the present application further provides a positioning apparatus. The positioning device includes: the receiving module is used for receiving uplink signal measurement data sent by a target base station, wherein the uplink signal measurement data comprises AOA data and TOA data; the calculation module is used for inputting the uplink signal measurement data and the first positioning data of the target terminal at the previous moment into the first positioning state space model, and solving the first positioning state space model based on a preset algorithm to obtain the first positioning data of the target terminal at the current moment; the first positioning state space model comprises a first positioning observation model and a first positioning state model, the first positioning observation model comprises a first incident angle observation model and a first three-dimensional TOA observation model, the first incident angle observation model is used for representing the relation between AOA data and first antenna data of a target terminal and second antenna data of a target base station, the first three-dimensional TOA observation model is used for representing the relation between the TOA data and the first antenna data and the second antenna data, and the first positioning state model is used for representing the relation between first positioning data of the current moment of the target terminal and first positioning data of the last moment of the target terminal.

In one embodiment, the state transition matrix at the previous time and the noise input matrix at the previous time are obtained according to the tracking sampling interval.

In one embodiment, the antenna position data of the target terminal includes position coordinates of a phase center of the antenna of the target terminal in the local rectangular coordinate system; the antenna position data of the target base station comprises position coordinates of a phase center of the antenna of the target base station in a local rectangular coordinate system; the antenna attitude data of the target base station includes an attitude angle of the antenna of the target base station in a local rectangular coordinate system.

In one embodiment, the positioning device further comprises: the acquisition module is used for acquiring inertia measurement data of the target terminal; the calculation module is further used for inputting the uplink signal measurement data, the inertia measurement data and second positioning data of the target terminal at a previous moment into a second positioning state space model, and solving the second positioning state space model based on a preset algorithm to obtain second positioning data of the target terminal at the current moment; the second positioning state space model comprises a second positioning observation model and a second positioning state model, the second positioning observation model comprises a second incidence angle observation model and a second three-dimensional TOA observation model, the second incidence angle observation model is used for representing the relation between AOA data and first antenna data and between the AOA data and the second antenna data, the second three-dimensional TOA observation model is used for representing the relation between the TOA data and the first antenna data and between the TOA data and the second antenna data, and the second positioning state model is used for representing the relation between second positioning data of the current moment of the target terminal and second positioning data and inertial measurement data of the last moment of the target terminal.

In one embodiment, the apparatus further comprises: constructing a target state equation between the first order differential of the second positioning data of the target terminal at the current moment and the second positioning data and the inertia measurement data of the target terminal at the current moment; and carrying out discretization processing on the target state equation to obtain a second positioning state model.

In one embodiment, the second positioning data comprises terminal attitude data, terminal speed data and terminal position data, and the target state equation comprises a first state equation, a second state equation and a third state equation; the first state equation is a state equation between first order differential of terminal attitude data of the target terminal at the current moment and the terminal attitude data and inertial measurement data of the target terminal at the current moment; the second state equation is a state equation between the first order differential of the terminal speed data of the target terminal at the current moment and the terminal speed data and the inertia measurement data of the target terminal at the current moment; the third state equation is a state equation between the first-order differential of the terminal position data of the target terminal at the current time and the terminal position data and the inertia measurement data of the target terminal at the current time.

In one embodiment, the inertial measurement data includes: white noise of a gyroscope carried on a target terminal on a carrier coordinate system, a rotation matrix between the carrier coordinate system and a navigation coordinate system, white noise of an accelerometer carried on the target terminal on the carrier coordinate system, a projection component of an angular velocity of the carrier coordinate system relative to the navigation coordinate system on the carrier coordinate system, random constant drift of the gyroscope on the carrier coordinate system, specific force output by the accelerometer on the carrier coordinate system and random constant drift of the accelerometer on the carrier coordinate system.

In one embodiment, the calculation module is specifically configured to obtain a constraint between the first antenna data and the second antenna data; and solving the first positioning state space model based on a preset algorithm and a constraint condition to obtain the first positioning data of the target terminal at the current moment.

In one embodiment, the calculating module is further specifically configured to solve the second positioning state space model based on a preset algorithm to obtain second positioning data of the target terminal at the current time, and the calculating module includes: acquiring constraint conditions between the first antenna data and the second antenna data; and solving the second positioning state space model based on a preset algorithm and a constraint condition to obtain second positioning data of the target terminal at the current moment.

In one embodiment, the predetermined algorithm is a particle filter algorithm.

In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of the method of any of the first aspect described above when the computer program is executed by the processor.

In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method of any of the above-mentioned first aspects.

In a fifth aspect, the present application further provides a computer program product. The computer program product comprising a computer program that when executed by a processor implements the steps of the method of any of the first aspects described above.

According to the positioning method, the positioning device, the computer equipment, the storage medium and the program product, uplink signal measurement data sent by a target base station are received, the uplink signal measurement data comprise AOA data and TOA data, then the uplink signal measurement data and first positioning data at a previous moment of a target terminal are input into a first positioning state space model, the first positioning state space model is solved based on a preset algorithm, and the first positioning data at the current moment of the target terminal are obtained; the first positioning state space model comprises a first positioning observation model and a first positioning state model, the first positioning observation model comprises a first incident angle observation model and a first three-dimensional TOA observation model, the first incident angle observation model is used for representing the relation between AOA data and first antenna data of a target terminal and second antenna data of a target base station, the first three-dimensional TOA observation model is used for representing the relation between the TOA data and the first antenna data and the second antenna data, and the first positioning state model is used for representing the relation between first positioning data of the current moment of the target terminal and first positioning data of the last moment of the target terminal. According to the method and the device, the height between the target terminal and the target base station can be considered through the first incident angle observation model and the first three-dimensional TOA observation model, and then the positioning precision is improved in the 2D positioning of the far and near field terminal.

Drawings

FIG. 1 is a flow chart illustrating a positioning method according to an embodiment;

FIG. 2 is a diagram of an exemplary location method;

FIG. 3 is a schematic flow chart of another positioning method in one embodiment;

FIG. 4 is a schematic flow chart illustrating a process for solving a first positioning state space model based on a particle filter algorithm according to an embodiment;

FIG. 5 is a schematic flow chart illustrating a process for solving a second localization state space model based on a particle filtering algorithm in one embodiment;

FIG. 6 is a simulation of two positioning algorithms in a near field case in one embodiment;

FIG. 7 is a simulation of two positioning algorithms in a far field case in one embodiment;

FIG. 8 is a block diagram of a positioning device in accordance with one embodiment;

FIG. 9 is a diagram of the internal structure of a computer device, in one embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

In an embodiment, as shown in fig. 1, a flowchart of a positioning method is provided, and this embodiment is applied to a terminal for example, it is understood that the method may also be applied to a server, and may also be applied to a system including the terminal and the server, and is implemented through interaction between the terminal and the server, where the terminal may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, and the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart car-mounted devices, and the like. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, and the like. The server may be implemented as a stand-alone server or as a server cluster consisting of a plurality of servers. In the embodiment of the application, the method comprises the following steps:

step 101, receiving uplink signal measurement data sent by a target base station, where the uplink signal measurement data includes AOA data and TOA data.

102, inputting uplink signal measurement data and first positioning data of a target terminal at a previous moment into a first positioning state space model, and solving the first positioning state space model based on a preset algorithm to obtain the first positioning data of the target terminal at the current moment; the first positioning state space model comprises a first positioning observation model and a first positioning state model, the first positioning observation model comprises a first incidence angle observation model and a first three-dimensional TOA observation model, the first incidence angle observation model is used for representing the relation between AOA data and first antenna data of a target terminal and second antenna data of a target base station, the first three-dimensional TOA observation model is used for representing the relation between the TOA data and the first antenna data and the second antenna data, and the first positioning state model is used for representing the relation between first positioning data of the current moment of the target terminal and first positioning data of the previous moment of the target terminal.

Optionally, as shown in fig. 2, an application environment diagram of the positioning method is provided, where the target base station is a single wireless base station, and after the target terminal sends an uplink signal to the target base station, the target base station measures the received uplink signal to obtain AOA data and TOA data, and sends the AOA data, the TOA data, and second antenna data of the target base station to the target terminal through an antenna, where the target terminal includes an antenna and a Central Processing Unit (CPU), the antenna is configured to receive the multiple data sent by the target base station, and the CPU is configured to calculate first positioning data of the target terminal at the current time according to the AOA data, the TOA data, and positioning data of the target terminal at the previous time.

In addition, the target base station may also send the AOA data, the TOA data, and the second antenna data of the target base station to the server, and the server calculates the first positioning data of the target terminal at the current time according to the AOA data, the TOA data, and the first positioning data of the target terminal at the previous time.

Optionally, the first positioning state space model is

x _k ＝F _k-1 x _k-1 +G _k-1 w _k-1 (1)

ξ _k ＝ψ(x _k )+υ _k (2)

Wherein

ψ(x _k )＝[h(x _k )g(x _k )] ^T (6)

α＝cosψcosγ-sinψsinθsinγ (9)

β＝sinψcosγ+cosψsinθsinγ (10)

υ _k ＝[v _k τ _k ] ^T (13)

The above formula (1) is a first positioning state Model, the formula (2) is a first positioning Observation Model, the formula (14) is a first Incident Angle Observation Model (IAOM), and the formula (15) is a first three-dimensional TOA Observation Model. x is the number of _k First positioning data, x, representing the current time (time k) of the target terminal _k-1 Is the first positioning data at the last time instant (time instant k-1) of the target terminal. The definitions of the amounts in equations (1) to (16) are as follows:

and &>

The first antenna data of the target terminal is antenna position data of the target terminal, specifically a horizontal 2D coordinate of an antenna phase center of the target terminal in a local rectangular coordinate system at the moment k; />

And &>

Respectively representing the speed components of the antenna phase center of the target terminal at the k moment on the x axis and the y axis of a local rectangular coordinate system; f _k-1 A state transition matrix at the moment of k-1; g _k-1 A noise input matrix at the k-1 moment; w is a _k-1 Is the state noise vector at the time k-1; t is a tracking sampling interval; xi _k The AOA and TOA observation vectors of the target base station measurement at the k moment are represented; />

The method comprises the steps that the incidence angle of an antenna phase center of a target terminal at the moment k in an antenna coordinate system of a target base station is represented, namely AOA data measured by the target base station at the moment k; />

TOA data measured by the target base station at the time k; psi (x) _k ) Representing an initial AOA and an initial TOA observation vector measured by the target terminal at the time k; h (x) _k ) Initial AOA data measured for the target terminal at the moment k; g (x) _k ) Initial TOA data measured by the target terminal at the moment k, wherein the light speed c is omitted; ψ, θ, γ and +>

Is second antenna data of the target base station, where ψ, θ and γ are antenna attitude data of the target base station, specifically attitude angles of the antenna of the target base station in the local rectangular coordinate system, which are azimuth angle, pitch angle and roll angle, respectively>

And &>

The antenna position data of the target base station is specifically the position coordinates of the antenna phase center of the target base station in a local rectangular coordinate system; />

The first antenna data of the target terminal is antenna position data of the target terminal, and specifically is a height coordinate of an antenna phase center of the target terminal in a local rectangular coordinate system at the moment k; arccos (·) is an inverse cosine function; upsilon is _k Is the total observed noise vector, v, of the target base station at time k _k AOA observation noise, τ, for target base station at time k _k Observing noise for the TOA of the target base station at the time k;

the AOA data and TOA data measured by the target base station, the second antenna data of the target base station and the first positioning data of the target terminal at the previous moment are input into the formulas (1) and (2), and then the first positioning state space model is solved based on the Bayesian filtering algorithm, so that the first positioning data of the target terminal at the current moment can be obtained.

To sum up, uplink signal measurement data sent by the target base station is received, wherein the uplink signal measurement data comprises AOA data and TOA data, then the uplink signal measurement data and the first positioning data of the target terminal at the previous moment are input into the first positioning state space model, and the first positioning state space model is solved based on a preset algorithm to obtain the first positioning data of the target terminal at the current moment; the first positioning state space model comprises a first positioning observation model and a first positioning state model, the first positioning observation model comprises a first incident angle observation model and a first three-dimensional TOA observation model, the first incident angle observation model is used for representing the relation between AOA data and first antenna data of a target terminal and second antenna data of a target base station, the first three-dimensional TOA observation model is used for representing the relation between the TOA data and the first antenna data and the second antenna data, and the first positioning state model is used for representing the relation between first positioning data of the current moment of the target terminal and first positioning data of the last moment of the target terminal. According to the method and the device, the height between the target terminal and the target base station can be considered through the first incident angle observation model and the first three-dimensional TOA observation model, and then the positioning precision is improved in the 2D positioning of the far and near field terminal.

Wherein x in formula (1) and formula (16) _k Is the first positioning data of the current time of the target terminal, x in formula (1) _k-1 Is the first positioning data of the last moment of the target terminal, F in formula (1) and formula (3) _k-1 Is the state transition matrix at the previous time, G in equations (1) and (4) _k-1 Is the noise input matrix at the previous moment, w in equation (1) _k-1 Is the state noise vector at the previous time, equation (3) and equation (b)4) T in (1) is the tracking sample interval. By changing the value of T at the target terminal, the state transition matrix at the previous time and the noise input matrix at the previous time can be changed, thereby changing the first positioning data at the current time of the target terminal.

In one embodiment, the antenna position data of the target terminal includes position coordinates of a phase center of the antenna of the target terminal in the local rectangular coordinate system; the antenna position data of the target base station comprises position coordinates of a phase center of the antenna of the target base station in a local rectangular coordinate system; the antenna attitude data of the target base station includes an attitude angle of the antenna of the target base station in the local rectangular coordinate system.

Wherein in the first positioning state space model

And &>

For the position coordinates of the antenna phase center of the target terminal at the moment k in a local rectangular coordinate system, and then combining>

And &>

Psi, theta and gamma are attitude angles of the antenna of the target base station in the local rectangular coordinate system.

In one embodiment, as shown in fig. 3, a flow chart of another positioning method is provided, the method comprising the steps of:

step 301, receiving uplink signal measurement data sent by a target base station, and acquiring inertial measurement data of a target terminal to acquire inertial measurement data of the target terminal, wherein the uplink signal measurement data includes AOA data and TOA data;

step 302, inputting uplink signal measurement data, inertia measurement data and second positioning data of the target terminal at the previous moment into a second positioning state space model, and solving the second positioning state space model based on a preset algorithm to obtain second positioning data of the target terminal at the current moment; the second positioning state space model comprises a second positioning observation model and a second positioning state model, the second positioning observation model comprises a second incidence angle observation model and a second three-dimensional TOA observation model, the second incidence angle observation model is used for representing the relation between AOA data and first antenna data and between the AOA data and the second antenna data, the second three-dimensional TOA observation model is used for representing the relation between the TOA data and the first antenna data and between the TOA data and the second antenna data, and the second positioning state model is used for representing the relation between second positioning data of the current moment of the target terminal and second positioning data and inertial measurement data of the last moment of the target terminal.

Optionally, the target base station is a single wireless base station, after the target terminal sends an uplink signal to the target base station, the target base station may measure the received uplink signal to obtain AOA data and TOA data, and send the AOA data, the TOA data, second antenna data of the target base station to the target terminal through the antenna, the target terminal includes an antenna, a CPU and an IMU, the antenna is configured to receive the multiple data sent by the target base station, the IMU includes a gyroscope and an accelerometer, and is configured to measure inertial measurement data of the target terminal, and the CPU is configured to calculate second positioning data of the target terminal at the current time according to the AOA data, the TOA data, the inertial measurement data and second positioning data of the target terminal at the previous time.

In addition, the target base station may also send the AOA data, the TOA data, and the second antenna data of the target base station to the server, the target terminal also sends the inertial measurement data to the server, and the server calculates second positioning data of the target terminal at the current time according to the AOA data, the TOA data, the inertial measurement data, and second positioning data of the target terminal at the previous time.

Optionally, the target state equation is

/>

Wherein the content of the first and second substances,

represents the first differential of x (t); psi, theta and gamma are respectively an azimuth angle, a pitch angle and a roll angle of the carrier coordinate system relative to an east-north-sky navigation coordinate system; v. of ^E 、v _N And v _U Respectively the east-direction speed, the north-direction speed and the sky-direction speed of the target terminal in an east-north-sky navigation coordinate system; l, lambda and h are respectively latitude, longitude and altitude of the target terminal in the geocentric coordinate system; epsilon _bx 、ε _by 、ε _bz 、/>

And &>

For inertial measurement data, of which epsilon _bx 、ε _by And ε _bz Respectively drift in the x-axis direction, the y-axis direction and the z-axis direction of a gyroscope on a target terminal in a carrier coordinate system, and combine the values in the x-axis direction, the y-axis direction and the z-axis direction>

And &>

Respectively drift and are the random constant values of the accelerometer on the target terminal in the directions of the x axis, the y axis and the z axis in the carrier coordinate system>

As a rotation matrix between the carrier coordinate system and the navigation coordinate system, 0 _1×9 Represents a pure zero row vector of dimension 9, which is greater than or equal to>

And &>

White noise in the directions of the x axis, the y axis and the z axis of the gyroscope in a carrier coordinate system, and the white noise is greater or less than>

And &>

White noise of the accelerometer in the directions of an x axis, a y axis and a z axis in a carrier coordinate system respectively; w (t) represents the total measurement white noise of the IMU of the target terminal at time t.

The specific meaning of f [. Cndot. ] is seen in equations (22) to (27)

/>

Wherein the content of the first and second substances,

and f ^bx 、f ^by 、f ^bz For the inertial measurement data, is>

And &>

The projection components of the angular velocity of the carrier coordinate system relative to the east-north-sky navigation coordinate system in the x-axis, y-axis and z-axis directions in the carrier coordinate system, f ^bx 、f ^by And f ^bz The specific forces output by the accelerometer in the directions of the x axis, the y axis and the z axis in the carrier coordinate system respectively, g is the gravity acceleration, and omega is _ie Is the angular velocity of rotation, R, of the earth _M Is the radius of curvature of the meridian of the earth, R _N The main curvature radius of the earth-unitary fourth of twelve earthly branches.

The target state equation is discretized by using a Euler discretization method, so that a second positioning state model can be obtained, wherein the second positioning state model is as follows:

x _k ＝F _k-1 x _k-1 +w _k-1 (28)

wherein x is _k Status information, x, indicating the current time (k time) of the target terminal _k-1 Is the state information of the last time (time k-1) of the target terminal, F _k-1 Is a function f [. DEG C]In discrete form, w _k-1 Is a discrete form of w (t), psi _k θ _k γ _k v _E,k v _N,k v _U,k L _k λ _k h _k ε _bx,k ε _by,k ε _bz,k

And the second positioning data is the current time (k time) of the target terminal at the k time.

Optionally, the second positioning observation model is

ξ _k ＝ψ[Ξ(x _k )]+υ _k (30)

/>

α＝cosψcosγ-sinψsinθsinγ (37)

β＝sinψcosγ+cosψsinθsinγ (38)

υ _k ＝[v _k τ _k σ _k ] ^T (41)

Equations (43) and (44) are the second incident angle observation model and the second three-dimensional TOA observation model, respectively; wherein ξ _k The AOA and TOA observation vectors of the target base station measurement at the k moment are represented;

TOA data measured by the target base station at the time k; psi (x) _k ) Representing an initial AOA and an initial TOA observation vector measured by the target terminal at the time k; h (X) _k ) Initial AOA data measured for the target terminal at the moment k; g (X) _k ) Initial TOA data measured by the target terminal at the moment k, wherein the light speed c is omitted; h (x) _k ) Means ofIs h (x) _k )；g(x _k ) The meaning of (A) is g (x) _k ) (ii) a ψ, θ, γ and +>

Is the second antenna data of the target base station, where ψ, θ and γ are the antenna attitude data of the target base station, specifically the antenna attitude data of the antenna of the target base station in the local rectangular coordinate system, which are azimuth angle, pitch angle and roll angle, respectively>

And &>

The antenna position data of the target base station, specifically the antenna position data of the antenna phase center of the target base station in the local rectangular coordinate system;

and &>

The first antenna data of the target terminal is antenna position data of the target terminal, specifically antenna position data of an antenna phase center of the target terminal in a local rectangular coordinate system at the moment k; arccos (g) is an inverse cosine function; upsilon is _k Is the total observed noise vector, v, of the target base station at time k _k AOA observation noise, τ, for target base station at time k _k Observing noise for the TOA of the target base station at the time k; />

The height coordinate of the antenna phase center of the target terminal at the moment k in a local rectangular coordinate system;

known error-containing quantity sigma measured for target base station _k The height coordinate, sigma, of the antenna phase center of the target terminal at time k in the local rectangular coordinate system _k Representing the corresponding observed noise; f is 1/298.257223563; l is _k 、λ _k And h _k Latitude, longitude and height of antenna phase center of target terminal at time k in geocentric geostationary coordinate system respectively>

The coordinate conversion function representing the transformation from the geocentric geostationary coordinate system to the local rectangular coordinate system can be directly obtained from the origin coordinate of the local rectangular coordinate system, namely ^ is greater than or equal to>

Wherein the content of the first and second substances,

and &>

Is the coordinate of the origin of the local rectangular coordinate system in the geocentric geostationary coordinate system, L ₀ And λ ₀ Respectively the latitude and longitude, x, of the origin of the rectangular coordinate system in the geocentric geostationary coordinate system ^e 、y ^e And z ^e The argument is the coordinates of the target terminal in the directions of the x-axis, y-axis and z-axis in the geocentric geostationary coordinate system.

Thus, the two-position state space model is

x _k ＝F _k-1 x _k-1 +w _k-1

ξ _k ＝ψ[Ξ(x _k )]+υ _k (46)

Measuring the AOA data, TOA data and angular velocity of the target base station

Specific force f ^bx 、f ^by 、f ^bz And inputting the second positioning data of the target terminal at the previous moment into a formula (46), and solving the second positioning state space model based on a Bayesian filter algorithm to obtain the second positioning data of the target terminal at the current moment.

In one embodiment, the positioning method further comprises: constructing a target state equation between the first-order differential of the second positioning data of the target terminal at the current moment and the second positioning data and the inertia measurement data of the target terminal at the current moment; and carrying out discretization treatment on the target state equation to obtain a second positioning state model.

Wherein, the first-order differential of the second positioning data of the current moment of the target terminal is

The second positioning data of the target terminal at the current moment is psi theta gamma v _E v _N v _U L λ h ε _bx ε _by ε _bz ▽ _bx ▽ _by ▽ _bz Inertial measurement data of->

And f ^bx 、f ^by 、f ^bz The constructed target state equations are equations (17) to (27). Then, the target state equation is discretized by using a Euler discretization method, and a positioning state model of equations (28) and (29) is obtained.

In one embodiment, the inertial measurement data comprises: the method comprises the following steps of white noise of a gyroscope carried on a target terminal on a carrier coordinate system, a rotation matrix between the carrier coordinate system and a navigation coordinate system, white noise of an accelerometer carried on the target terminal on the carrier coordinate system, a projection component of angular velocity of the carrier coordinate system relative to the navigation coordinate system on the carrier coordinate system, random constant drift of the gyroscope on the carrier coordinate system, specific force output by the accelerometer on the carrier coordinate system and random constant drift of the accelerometer on the carrier coordinate system.

Wherein the terminal attitude data are ψ, θ and γ in equations (18) to (22), which represent the azimuth angle, the pitch angle and the roll angle of the carrier coordinate system with respect to the "east-north-sky" navigation coordinate system, respectively. The terminal velocity data is v in formula (18), formula (19) and formulae (23) to (25) _E 、v _N And v _U And respectively representing the east-direction speed, the north-direction speed and the sky-direction speed of the target terminal in an east-north-sky navigation coordinate system. The terminal position data are L, λ, and h in formula (18), formula (19), formula (24), and formula (25), which respectively represent the latitude, longitude, and altitude of the target terminal in the geocentric-geocentric coordinate system. The inertial measurement data includes those in equations (17) to (27)

f ^bx 、f ^by 、f ^bz 、ε _bx 、ε _by 、ε _bz 、/>

And &>

Wherein->

And &>

White noise of a gyroscope carried on a target terminal on a carrier coordinate system is represented; />

And &>

White noise of an accelerometer carried on a target terminal on a carrier coordinate system is represented;

and &>

Representing the projection component of the angular velocity of the carrier coordinate system relative to the navigation coordinate system on the carrier coordinate system; f. of ^bx 、f ^by And f ^bz Representing the specific force output by the accelerometer on the carrier coordinate system; epsilon _bx 、ε _by And epsilon _bz Representing the random constant drift of the gyroscope on a carrier coordinate system; />

And &>

Representing the random constant drift of the accelerometer on a carrier coordinate system; />

Representing a rotation matrix between the carrier coordinate system and the navigation coordinate system. It should be noted that the positioning data and the inertia measurement data with the subscript k in the formula (29) have the same meanings as the corresponding quantities described above, and are both the positioning data and the inertia measurement data at the time of k, such as ψ _k 、θ _k And gamma _k And respectively representing the azimuth angle, the pitch angle and the roll angle of the carrier coordinate system at the moment k relative to an east-north-sky navigation coordinate system.

In addition, the first state equation is formula (22); the second state equation is equation (23); the third state equation is (25).

For ease of reading, equations (22), (23) and (25) are again set forth below.

In one embodiment, solving the first positioning state space model based on a preset algorithm to obtain first positioning data of the target terminal at the current time includes: acquiring a constraint condition between the first antenna data and the second antenna data; and solving the first positioning state space model based on a preset algorithm and a constraint condition to obtain first positioning data of the target terminal at the current moment.

In one embodiment, the predetermined algorithm is a particle filter algorithm.

Optionally, the constraint condition is

Wherein

And &>

For the first antenna data ψ, θ @>

And &>

For the second antenna data, the other quantities in equation (48) are explained below both equation (27) and equation (44). .

As shown in fig. 4, a schematic flow chart of solving the first positioning state space model based on the particle filter algorithm is provided, and the process of solving the first positioning state space model based on the particle filter algorithm and the constraint condition is as follows:

obtaining a prior probability density function p (x) according to the AOA and TOA measured values of the target base station ₀ ) Then the particle filter initialization is performed, i.e. from the prior probability density function p (x) ₀ ) Extracting Lambda particles

An initial particle weight of ^ 5>

Then importance sampling is carried out, and Lambda particles are obtained by using formula (12)

Namely, it is

Then, the particle weight is updated

Wherein the content of the first and second substances,

is particle->

Likelihood function of, xi _k And &>

Has the same meaning as in the formulae (5) to (15), R _k Is the observed noise vector v of the target base station _k Is selected based on the covariance matrix, < > is selected>

For the normalized weights, a subsequent resampling results in a new set of particles->

Corresponding particle weight is +>

Resampling means that after the particles are subjected to weight updating, namely formula (53), the particles are copied proportionally from large to small according to the updated weight of the particles until the particle number reaches lambada. For example, if there are 5 particles and their updated weights are 0.6,0.4, 0, respectively, then after resampling, 3 particles out of the 5 particles are 0.6 corresponding particles and 2 particles are 0.4 corresponding particles. Finally, the first positioning data of the current time of the target terminal can be expressed as

In one embodiment, solving the second positioning state space model based on a preset algorithm to obtain second positioning data of the target terminal at the current time includes: acquiring constraint conditions between the first antenna data and the second antenna data; and solving the second positioning state space model based on a preset algorithm and a constraint condition to obtain second positioning data of the target terminal at the current moment.

Optionally, as shown in fig. 5, a schematic flow chart of solving the second positioning state space model based on the particle filter algorithm is provided, and a process of solving the second positioning state space model based on the particle filter algorithm and the constraint condition is as follows:

Initial particle weight of +>

Importance sampling is then performed to obtain Λ particles using equation (28)

Namely, it is

Then the particle weight is updated

Wherein, the first and the second end of the pipe are connected with each other,

is particle->

Likelihood function of, xi _k And &>

Has the same meaning as in the formulae (30) to (34), R _k Is the observed noise vector v of the target base station _k In the covariance matrix of (c), based on the covariance matrix of the cell in the preceding block>

For normalized weights, a subsequent resampling results in a new set of particles->

Corresponding particle weight of

Resampling means that after the particles are subjected to weight updating, namely formula (59), the particles are copied proportionally from large to small according to the updated weight of the particles until the particle number reaches lambada.

Finally, the second positioning data of the current time of the target terminal can be expressed as

In addition, during the above calculation, the AOA and TOA measurements xi of the target base station are used _k The target base station side needs to calculate firstly and then sends the calculated value to the target terminal, so that the target terminal receives the AOA and TOA measured value xi of the target base station _k There is a certain time delay, and since the time delay can be estimated in advance for its length range, it is possible to store in the target terminal a time from the current time to a previous time (earlier than the AOA and TOA measurement values ξ of the target base station) against this problem _k Time of occurrence), AOA and TOA measurements η at the target base station _k When the terminal is reached, the AOA and TOA measured values xi of the target base station can be carried out according to the stored inertial measurement data _k The location data of the target terminal at the time of occurrence is estimated, and then the location data of the target terminal at the current time is inferred using the stored inertial measurement data.

The validity of the method is proved through simulation truth verification, and the position coordinate of the target base station is set to be

And &>

Attitude angles are ψ =270 °, θ =0 and γ =0, standard deviation of AOA observation error of the target base station is 1 degree, standard deviation of TOA observation error is 1 meter, the number of particles of the particle filter is set to 500, and 100 monte carlo simulations are performed in total.

Under the condition of near field, the positioning performance analysis of the traditional single base station positioning algorithm and the single base station positioning algorithm is carried out, wherein the initial positioning data of the target terminal is set to be

As shown in fig. 6, the simulation diagrams of two positioning algorithms under the near field condition are shown, where an average root mean square positioning error of a conventional single base station positioning algorithm is 1.83m, and an average root mean square positioning error of the single base station positioning algorithm of the present application is 0.308m, so that under the near field condition, the present application has higher positioning accuracy in 2D positioning of the terminal, the positioning performance is greatly improved, and the problem of large positioning error of the conventional single base station positioning algorithm under the near field condition can be solved.

Under far field condition, the positioning performance analysis of the traditional single base station positioning algorithm and the single base station positioning algorithm is carried out, wherein the initial positioning data of the target terminal is set to be

As shown in fig. 7, the simulation diagram is a simulation diagram of two positioning algorithms in a far field, where an average root mean square positioning error of a conventional single base station positioning algorithm is 1.10m, and an average root mean square positioning error of the single base station positioning algorithm is 1.04m, so that the present application also has higher positioning accuracy in terminal 2D positioning in the far field, and therefore, the present application can improve positioning accuracy in terminal 2D positioning.

In summary, the present application is implemented in the following detail: firstly, receiving uplink signal measurement data sent by a target base station, wherein the uplink signal measurement data comprises AOA data and TOA data. Inputting uplink signal measurement data and first positioning data of a target terminal at the previous moment into a first positioning state space model, acquiring constraint conditions between first antenna data and second antenna data, and solving the first positioning state space model based on a particle filter algorithm to obtain the first positioning data of the target terminal at the current moment; the first positioning state space model comprises a first positioning observation model and a first positioning state model, the first positioning observation model comprises a first incidence angle observation model and a first three-dimensional TOA observation model, the first incidence angle observation model is used for representing the relation between AOA data and first antenna data of a target terminal and second antenna data of a target base station, the first three-dimensional TOA observation model is used for representing the relation between the TOA data and the first antenna data and the second antenna data, and the first positioning state model is used for representing the relation between first positioning data of the current moment of the target terminal and first positioning data of the previous moment of the target terminal. The first positioning state model is specifically used for representing the relationship between the first positioning data of the target terminal at the current moment and the first positioning data of the target terminal at the previous moment as well as the state transition matrix, the noise input matrix and the state noise vector of the previous moment, and the state transition matrix and the noise input matrix of the previous moment are obtained according to the tracking sampling interval. The first antenna data includes antenna position data of the target terminal, and the second antenna data includes antenna position data and antenna attitude data of the target base station. The antenna position data of the target terminal comprises position coordinates of the phase center of the antenna of the target terminal in a local rectangular coordinate system; the antenna position data of the target base station comprises position coordinates of a phase center of the antenna of the target base station in a local rectangular coordinate system; the antenna attitude data of the target base station includes an attitude angle of the antenna of the target base station in a local rectangular coordinate system.

In addition, another most detailed embodiment of the present application is as follows: firstly, receiving uplink signal measurement data sent by a target base station, and acquiring inertial measurement data of a target terminal, wherein the uplink signal measurement data comprises AOA data and TOA data, and the inertial measurement data comprises: white noise of a gyroscope carried on a target terminal on a carrier coordinate system, a rotation matrix between the carrier coordinate system and a navigation coordinate system, white noise of an accelerometer carried on the target terminal on the carrier coordinate system, a projection component of an angular velocity of the carrier coordinate system relative to the navigation coordinate system on the carrier coordinate system, random constant drift of the gyroscope on the carrier coordinate system, specific force output by the accelerometer on the carrier coordinate system and random constant drift of the accelerometer on the carrier coordinate system. And then inputting the uplink signal measurement data, the inertia measurement data and second positioning data of the target terminal at a previous moment into a second positioning state space model. Finally, constraint conditions between the first antenna data and the second antenna data are obtained, and the second positioning state space model is solved based on a particle filter algorithm to obtain second positioning data of the target terminal at the current moment; the second positioning state space model comprises a second positioning observation model and a second positioning state model, the second positioning observation model comprises a second incidence angle observation model and a second three-dimensional TOA observation model, the second incidence angle observation model is used for representing the relation between AOA data and first antenna data and the relation between the AOA data and the first antenna data, the second three-dimensional TOA observation model is used for representing the relation between the TOA data and the first antenna data and the relation between the second antenna data, and the second positioning state model is used for representing the relation between second positioning data of the current moment of the target terminal and second positioning data and inertia measurement data of the last moment of the target terminal. The construction process of the positioning state model comprises the following steps: constructing a target state equation between the first order differential of the second positioning data of the target terminal at the current moment and the second positioning data and the inertia measurement data of the target terminal at the current moment; and carrying out discretization treatment on the target state equation to obtain a second positioning state model. The second positioning data comprise terminal attitude data, terminal speed data and terminal position data, and the target state equation comprises a first state equation, a second state equation and a third state equation; the first state equation is a state equation between first-order differential of terminal attitude data of the target terminal at the current moment and the terminal attitude data and inertia measurement data of the target terminal at the current moment; the second state equation is a state equation between first-order differential of the terminal speed data of the target terminal at the current moment and the terminal speed data and inertia measurement data of the target terminal at the current moment; the third state equation is a state equation between the first order differential of the terminal position data of the target terminal at the current time and the terminal position data and the inertial measurement data of the target terminal at the current time.

In a word, the height between the target terminal and the target base station can be considered through the first incident angle observation model and the first three-dimensional TOA observation model or through the second incident angle observation model and the second three-dimensional TOA observation model, and then the positioning accuracy is improved in the 2D positioning of the far and near field terminal.

In addition, it should be noted that the first positioning state model, i.e. formula (1), and the first positioning observation model, i.e. formula (2), include AOA data and TOA data measured by the target base station, where formula (14) is a first IAOM, and formula (15) is a first 3D TOA observation model. The first positioning state model and the first positioning observation model form a first positioning state space model, so that the high-precision continuous 2D positioning of the target terminal can be realized only by using the AOA data and the TOA data of the target base station. Two aspects of ensuring that the first positioning state space model can realize continuous high-precision positioning in the 2D positioning of the far-near field terminal are that, on one hand, the height coordinate of the antenna phase center of the known k-time target terminal in the local rectangular coordinate system is obtained, and on the other hand, the constraint condition is formula (47).

In addition, the second positioning state model, i.e., formula (28), includes the measurement value of the IMU on the target terminal, and the second positioning observation model, i.e., formula (30), includes AOA data and TOA data measured by the target base station, where formula (43) is a second IAOM and formula (44) is a second 3D TOA observation model. The second positioning state model and the second positioning observation model form a second positioning state space model, namely a formula (29), so that a close coupling positioning algorithm of the AOA and the TOA of the target base station and the IMU of the target terminal is realized. Meanwhile, the AOA data and the TOA data of the target base station and the IMU measured value of the target terminal are used to realize high-precision continuous 2D positioning of the target terminal. Two aspects of ensuring that the second positioning state space model can realize continuous high-precision positioning in the 2D positioning of the far-near field terminal are provided, on one hand, the height coordinate of the antenna phase center of the known k-time target terminal containing error in the local rectangular coordinate system is the height coordinate

Another aspect is the constraint, equation (47). />

It should be understood that, although the steps in the flowcharts related to the embodiments as described above are sequentially displayed as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.

Based on the same inventive concept, the embodiment of the present application further provides a positioning apparatus for implementing the above-mentioned positioning method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the method, so the specific limitations in one or more embodiments of the positioning device provided below may refer to the limitations on the positioning method in the above, and details are not described here.

In one embodiment, as shown in fig. 8, a block diagram of a positioning apparatus is provided, where the positioning apparatus 800 includes: a receiving module 801 and a calculating module 802, wherein:

an accepting module 801, configured to receive uplink signal measurement data sent by a target base station, where the uplink signal measurement data includes AOA data and TOA data.

The calculation module 802 is configured to input uplink signal measurement data and first positioning data of a target terminal at a previous time into a first positioning state space model, and solve the first positioning state space model based on a preset algorithm to obtain the first positioning data of the target terminal at the current time; the first positioning state space model comprises a first positioning observation model and a first positioning state model, the first positioning observation model comprises a first incident angle observation model and a first three-dimensional TOA observation model, the first incident angle observation model is used for representing the relation between AOA data and first antenna data of a target terminal and second antenna data of a target base station, the first three-dimensional TOA observation model is used for representing the relation between the TOA data and the first antenna data and the second antenna data, and the first positioning state model is used for representing the relation between first positioning data of the current moment of the target terminal and first positioning data of the last moment of the target terminal.

In one embodiment, the positioning device further comprises: the acquisition module is used for acquiring inertia measurement data of the target terminal; the calculating module 802 is further configured to input the uplink signal measurement data, the inertia measurement data, and second positioning data of a previous moment of the target terminal into a second positioning state space model, and solve the second positioning state space model based on a preset algorithm to obtain second positioning data of the current moment of the target terminal; the second positioning state space model comprises a second positioning observation model and a second positioning state model, the second positioning observation model comprises a second incidence angle observation model and a second three-dimensional TOA observation model, the second incidence angle observation model is used for representing the relation between AOA data and first antenna data and the relation between the AOA data and the first antenna data, the second three-dimensional TOA observation model is used for representing the relation between the TOA data and the first antenna data and the relation between the second antenna data, and the second positioning state model is used for representing the relation between second positioning data of the current moment of the target terminal and second positioning data and inertia measurement data of the last moment of the target terminal.

In one embodiment, the apparatus further comprises: constructing a target state equation between the first order differential of the second positioning data of the target terminal at the current moment and the second positioning data and the inertia measurement data of the target terminal at the current moment; and carrying out discretization treatment on the target state equation to obtain a second positioning state model.

In one embodiment, the second positioning data comprises terminal attitude data, terminal speed data and terminal position data, and the target state equation comprises a first state equation, a second state equation and a third state equation; the first state equation is a state equation between first order differential of terminal attitude data of the target terminal at the current moment and the terminal attitude data and inertial measurement data of the target terminal at the current moment; the second state equation is a state equation between the first order differential of the terminal speed data of the target terminal at the current moment and the terminal speed data and the inertia measurement data of the target terminal at the current moment; the third state equation is a state equation between the first order differential of the terminal position data of the target terminal at the current time and the terminal position data and the inertial measurement data of the target terminal at the current time.

In one embodiment, the calculating module 802 is specifically configured to obtain a constraint between the first antenna data and the second antenna data; and solving the first positioning state space model based on a preset algorithm and a constraint condition to obtain the first positioning data of the target terminal at the current moment.

In one embodiment, the calculating module 802 is further specifically configured to solve the second positioning state space model based on a preset algorithm to obtain second positioning data of the target terminal at the current time, and includes: acquiring a constraint condition between the first antenna data and the second antenna data; and solving the second positioning state space model based on a preset algorithm and a constraint condition to obtain second positioning data of the target terminal at the current moment.

In one embodiment, the predetermined algorithm is a particle filter algorithm.

The modules in the positioning device can be wholly or partially implemented by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 9. The computer apparatus includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input device. The processor, the memory and the input/output interface are connected by a system bus, and the communication interface, the display unit and the input device are connected by the input/output interface to the system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The input/output interface of the computer device is used for exchanging information between the processor and an external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a positioning method. The display unit of the computer device is used for forming a visual visible picture, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.

Those skilled in the art will appreciate that the architecture shown in fig. 9 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In an embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the above method embodiments when executing the computer program.

In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.

In an embodiment, a computer program product is provided, comprising a computer program which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.

It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the relevant laws and regulations and standards of the relevant country and region.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware instructions of a computer program, which may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include a Read-Only Memory (ROM), a magnetic tape, a floppy disk, a flash Memory, an optical Memory, a high-density embedded nonvolatile Memory, a resistive Random Access Memory (ReRAM), a Magnetic Random Access Memory (MRAM), a Ferroelectric Random Access Memory (FRAM), a Phase Change Memory (PCM), a graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases involved in the embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A positioning method, characterized in that the positioning method comprises:

receiving uplink signal measurement data sent by a target base station, wherein the uplink signal measurement data comprises angle of arrival (AOA) data and time of arrival (TOA) data;

inputting the uplink signal measurement data and first positioning data of the target terminal at the previous moment into a first positioning state space model, and solving the first positioning state space model based on a preset algorithm to obtain the first positioning data of the target terminal at the current moment;

the first positioning state space model comprises a first positioning observation model and a first positioning state model, the first positioning observation model comprises a first incident angle observation model and a first three-dimensional TOA observation model, the first incident angle observation model is used for representing the relation between the AOA data and the first antenna data of the target terminal and the second antenna data of the target base station, the first three-dimensional TOA observation model is used for representing the relation between the TOA data and the first antenna data and the second antenna data, and the first positioning state model is used for representing the relation between the first positioning data of the current moment of the target terminal and the first positioning data of the last moment of the target terminal.

2. The method according to claim 1, wherein the first positioning state model is specifically used to characterize a relationship between the first positioning data of the target terminal at the current time and the first positioning data of the target terminal at the previous time and a state transition matrix, a noise input matrix at the previous time, and a state noise vector at the previous time.

3. The method of claim 2, wherein the state transition matrix at the previous time and the noise input matrix at the previous time are derived from tracking sampling intervals.

4. The method of claim 1, wherein the first antenna data comprises antenna position data for the target terminal, and wherein the second antenna data comprises antenna position data and antenna attitude data for the target base station.

5. The method of claim 4, wherein the antenna position data of the target terminal comprises position coordinates of a phase center of the antenna of the target terminal in a local rectangular coordinate system;

the antenna position data of the target base station comprises position coordinates of a phase center of an antenna of the target base station in a local rectangular coordinate system;

the antenna attitude data of the target base station comprises an attitude angle of the antenna of the target base station in a local rectangular coordinate system.

6. The method of claim 1, wherein the positioning method further comprises:

acquiring inertia measurement data of the target terminal;

inputting the uplink signal measurement data, the inertia measurement data and second positioning data of the target terminal at a previous moment into a second positioning state space model, and solving the second positioning state space model based on a preset algorithm to obtain second positioning data of the target terminal at the current moment;

the second positioning state space model comprises a second positioning observation model and a second positioning state space model, the second positioning observation model comprises a second incidence angle observation model and a second three-dimensional TOA observation model, the second incidence angle observation model is used for representing the relation between the AOA data and the first antenna data and the second antenna data, the second three-dimensional TOA observation model is used for representing the relation between the TOA data and the first antenna data and the second antenna data, and the second positioning state model is used for representing the relation between the second positioning data of the target terminal at the current moment and the second positioning data and the inertial measurement data of the target terminal at the moment.

7. The method of claim 6, wherein the positioning method further comprises:

constructing an object state equation between the first-order differential of the second positioning data of the target terminal at the current moment and the second positioning data and the inertia measurement data of the target terminal at the current moment;

and carrying out discretization processing on the target state equation to obtain the second positioning state model.

8. The method of claim 7, wherein the second positioning data comprises terminal attitude data, terminal velocity data, and terminal position data, and the objective state equations comprise a first state equation, a second state equation, and a third state equation;

the first state equation is a state equation between a first-order differential of the terminal attitude data of the target terminal at the current moment and the terminal attitude data and the inertia measurement data of the target terminal at the current moment;

the second state equation is a state equation between the first-order derivative of the terminal speed data of the target terminal at the current moment and the terminal speed data and the inertia measurement data of the target terminal at the current moment;

the third state equation is a state equation between the first-order derivative of the terminal position data of the target terminal at the current time and the terminal position data and the inertial measurement data of the target terminal at the current time.

9. The method of claim 8, wherein the inertial measurement data comprises: the system comprises a gyroscope carried on a target terminal, white noise on a carrier coordinate system, a rotation matrix between the carrier coordinate system and a navigation coordinate system, white noise of an accelerometer carried on the target terminal on the carrier coordinate system, a projection component of the carrier coordinate system on the carrier coordinate system relative to the angular velocity of the navigation coordinate system, random constant drift of the gyroscope on the carrier coordinate system, specific force output by the accelerometer on the carrier coordinate system and random constant drift of the accelerometer on the carrier coordinate system.

10. The method according to claim 1, wherein the solving the first positioning state space model based on a preset algorithm to obtain first positioning data of the target terminal at the current time includes:

obtaining constraints between the first antenna data and the second antenna data;

and solving the first positioning state space model based on the preset algorithm and the constraint condition to obtain the first positioning data of the target terminal at the current moment.

11. The method according to claim 6, wherein the solving the second positioning state space model based on a preset algorithm to obtain second positioning data of the target terminal at the current time includes:

and solving the second positioning state space model based on the preset algorithm and the constraint condition to obtain second positioning data of the target terminal at the current moment.

12. The method according to any one of claims 1 and 11, wherein the predetermined algorithm is a particle filter algorithm.

13. A positioning apparatus, for use in a target terminal, the positioning apparatus comprising:

a receiving module, configured to receive uplink signal measurement data sent by a target base station, where the uplink signal measurement data includes AOA data and TOA data;

the calculation module is used for inputting the uplink signal measurement data and first positioning data of the target terminal at the previous moment into a first positioning state space model, and solving the first positioning state space model based on a preset algorithm to obtain the first positioning data of the target terminal at the current moment;

14. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 12.

15. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 12.

16. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 12 when executed by a processor.