CN112887899A - Positioning system and positioning method based on single base station soft position information - Google Patents

Positioning system and positioning method based on single base station soft position information Download PDF

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CN112887899A
CN112887899A CN202110014749.1A CN202110014749A CN112887899A CN 112887899 A CN112887899 A CN 112887899A CN 202110014749 A CN202110014749 A CN 202110014749A CN 112887899 A CN112887899 A CN 112887899A
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positioning
base station
user
module
measurement
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CN112887899B (en
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沈渊
戈锋
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Luowei Zhilian Beijing Technology Co ltd
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Tsinghua University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/025—Services making use of location information using location based information parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management

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Abstract

The invention provides a positioning system and a positioning method based on single-base-station soft position information, wherein the system comprises the following steps: the positioning base station is used for receiving positioning data sent by the user terminal and generating a positioning result based on the positioning data; the user terminal is used for sending positioning data to the positioning base station and receiving a positioning result; the positioning base station comprises an antenna array, a clock synchronization module, a measurement module, a soft position information extraction module, a communication module and a positioning module, and the user terminal comprises a wireless measurement module, a communication module and an inertia measurement unit; the soft position information extraction module is used for extracting soft position information from the measurement information related to the position of the user; the positioning module is used for estimating the position of the user according to the soft position information and the inertial data of the user terminal. The positioning system and the positioning method provided by the invention can effectively solve the problem of phase ambiguity caused by the fact that the distance between the array antennas is larger than half wavelength in the positioning of the single base station, thereby obviously improving the positioning accuracy by increasing the aperture of the antenna array.

Description

Positioning system and positioning method based on single base station soft position information
Technical Field
The invention relates to the technical field of wireless communication, in particular to a positioning system and a positioning method based on single-base-station soft position information.
Background
Real-time and high-precision position information has important application in many fields, such as internet of things, intelligent factories, unmanned aerial vehicle positioning, emergency rescue and the like. Global Navigation Satellite Systems (GNSS) are capable of providing meter-level location information in open outdoor environments. However, the satellite signal has low signal strength and is difficult to penetrate buildings, so that the positioning stability in urban canyons is poor and the satellite cannot be used indoors. In order to meet the requirements of real-time high-precision positioning services, a variety of positioning technologies have appeared, such as a positioning technology based on radio frequency signals, a positioning technology based on inertial sensors, a positioning technology based on vision, and the like.
The most common positioning technologies in the prior art are based on radio frequency signals, including bluetooth, WiFi, RFID, ultra wideband, etc. These positioning technologies mostly utilize time of arrival (TOA), time difference of arrival (TDOA), Received Signal Strength (RSS) to achieve positioning. The method based on TOA and TDOA requires more than 3 base stations, and has certain requirements on the topology of base station deployment, and high-precision positioning can be realized only when the base stations are far away from each other. The TDOA-based location system also requires synchronization of the clock source of each base station, further increasing the complexity and cost of the location system. In addition, positioning technologies based on RSS include triangulation positioning and fingerprint positioning, which are difficult to achieve meter-level positioning accuracy even if a large number of base stations are deployed, and a large amount of manpower is required to maintain a fingerprint database in a fingerprint positioning manner.
In recent years, many wireless radio frequency signals provide the capability of phase measurement, such as ultra-wideband carrier phase measurement, Channel State Information (CSI) measurement for WiFi, fixed frequency extended signal (CTE) in bluetooth 5.1, and the like. When the base station is equipped with an antenna array, stable angle information can be obtained using a signal phase difference of arrival (PDOA) technique. The user can be positioned by utilizing a single base station by utilizing the angle information and combining the estimation of the distance, the number of the required base stations is obviously reduced, and the complexity and the cost of a positioning system are reduced. However, the angle measurement technology based on the PDOA requires that the antenna spacing is smaller than a half wavelength, otherwise the phase ambiguity problem occurs, namely the measured PDOA is limited to-180 degrees to 180 degrees. The antenna spacing smaller than half wavelength can significantly reduce the accuracy of the angle estimation algorithm, and the coupling effect between the antennas can seriously affect the angle measurement accuracy. In addition, the current angle measurement algorithm generally requires that array elements are arranged linearly or uniformly in a circular array.
Therefore, a new positioning system and a new positioning method are needed to solve the above existing problems.
Disclosure of Invention
The invention provides a positioning system and a positioning method based on single-base-station soft position information, which are used for solving the problem that the prior art cannot effectively solve the problem of phase ambiguity caused by the fact that the array antenna spacing is larger than half wavelength in single-base-station positioning.
In a first aspect, the present invention provides a positioning system based on single base station soft location information, including:
positioning a base station and a user terminal;
the positioning base station is used for receiving positioning data sent by the user terminal and generating a positioning result based on the positioning data;
and the user terminal is used for sending the positioning data to the positioning base station and receiving the positioning result.
In one embodiment, the positioning base station comprises an antenna array, a clock synchronization module, a positioning module, a base station communication module, a measurement module and a soft position information extraction module;
the antenna array is used for receiving a positioning signal transmitted by the user terminal;
the clock synchronization module is used for synchronizing clocks of all array elements in the antenna array;
the base station communication module is used for sending the positioning result to the user terminal;
the measurement module is used for extracting the associated measurement information of the user position from the signals received by the antenna array;
the soft position information extraction module is used for extracting soft position information from the associated measurement information;
the positioning module is configured to estimate a user position based on the extracted soft location information.
In one embodiment, the base station communication module is further configured to receive inertial data from the user terminal;
accordingly, the location module is further configured to estimate the user location from the inertial data.
In one embodiment, the measurement module comprises a distance measurement module and a phase measurement module;
the distance measurement module is used for extracting the distance from the user in the associated measurement information to any array element in the antenna array;
the phase measurement module is used for extracting the arrival phase from the user in the associated measurement information to each array element in the antenna array.
In one embodiment, the measurement module further comprises an auxiliary measurement module for measuring auxiliary positioning information.
In one embodiment, the user terminal comprises a wireless measurement module, a terminal communication module and an inertial measurement unit;
the wireless measurement module is used for transmitting the positioning signal to the positioning base station and receiving the positioning signal transmitted by the positioning base station;
the terminal communication module is used for receiving the positioning result sent by the positioning base station and sending inertial data to the positioning base station;
the inertia measurement unit is used for measuring the motion state of the user terminal.
In a second aspect, the present invention further provides a positioning method based on single base station soft location information, including:
acquiring an original positioning signal, and extracting associated measurement information from the original positioning signal;
approximating soft location information extracted from the associated measurement information based on a Gaussian mixture model;
estimating a user location based on the soft location information approximated by the Gaussian mixture model.
In an embodiment, the approximating soft location information extracted from the correlation measurement information based on a gaussian mixture model specifically includes:
extracting a fuzzy phase measurement result from the associated measurement information, and constructing a fuzzy likelihood function based on the fuzzy phase measurement result;
selecting a non-fuzzy measurement result from the associated measurement information, and constructing a non-fuzzy likelihood function based on the non-fuzzy measurement result;
estimating a mean value of each component based on the fuzzy likelihood function and the unambiguous likelihood function;
carrying out random sampling near the mean value of each component, and estimating a covariance matrix of the corresponding component;
and calculating the weight of each component based on the covariance matrix, and eliminating low-weight components to obtain the soft position information.
In one embodiment, the estimating the user location based on the soft location information approximated by the gaussian mixture model specifically includes:
defining state variables related to the user position, and constructing a state prediction equation and a state updating equation based on the state variables related to the user position;
calculating a probability distribution of the user position-dependent state variables based on the state prediction equation in combination with inertial data;
updating the probability distribution of the user location related state variables based on the state update equation in combination with the soft location information;
and repeating the state prediction and state updating process to obtain the user position estimation result.
In one embodiment, said calculating a probability distribution of said user position-dependent state variables based on said state prediction equation in combination with inertial data further comprises:
estimating the variation of the position of the user by combining the inertial data with a pedestrian dead reckoning algorithm;
or, the inertial data is combined with an inertial navigation algorithm to estimate the variation of the speed of the user.
According to the positioning system and the positioning method based on the single-base-station soft position information, the problem of phase ambiguity caused by the fact that the distance between the array antennas is larger than half wavelength in single-base-station positioning can be effectively solved through the positioning system and the positioning method, and therefore positioning accuracy can be remarkably improved by increasing the aperture of the antenna array.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic structural diagram of a positioning system based on single-base-station soft location information according to the present invention;
FIG. 2 is a schematic flow chart of a positioning method based on single-base-station soft location information according to the present invention;
FIG. 3 is a schematic flow chart of soft location information extraction provided by the present invention;
FIG. 4 is a schematic flow chart of estimating a user's location provided by the present invention;
FIG. 5 is a schematic diagram of soft location information approximated using a Gaussian mixture model according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
To solve the problems in the prior art, the present invention provides a positioning system based on single base station soft location information, as shown in fig. 1, including:
positioning a base station and a user terminal;
the positioning base station is used for receiving positioning data sent by the user terminal and generating a positioning result based on the positioning data;
and the user terminal is used for sending the positioning data to the positioning base station and receiving the positioning result.
Specifically, the positioning system based on the single-base-station soft position information provided by the invention comprises two parts, namely a positioning base station and a user terminal, wherein the positioning base station is used for receiving related information of positioning data sent by the user terminal and generating a positioning result, and the user terminal at the opposite end is used for generating the positioning data and receiving the positioning result returned by the positioning base station.
The positioning system provided by the invention can effectively solve the problem of phase ambiguity caused by the fact that the distance between the array antennas is larger than half wavelength in the positioning of the single base station, thereby obviously improving the positioning accuracy by increasing the aperture of the antenna array.
Based on the above embodiment, the positioning base station includes an antenna array, a clock synchronization module, a positioning module, a base station communication module, a measurement module, and a soft location information extraction module;
the antenna array is used for receiving a positioning signal transmitted by the user terminal;
the clock synchronization module is used for synchronizing clocks of all array elements in the antenna array;
the base station communication module is used for sending the positioning result to the user terminal;
the measurement module is used for extracting the associated measurement information of the user position from the signals received by the antenna array;
the soft position information extraction module is used for extracting soft position information from the associated measurement information;
the positioning module is configured to estimate a user position based on the extracted soft location information.
Wherein the base station communication module is further configured to receive inertial data from the user terminal;
accordingly, the location module is further configured to estimate the user location from the inertial data.
Wherein the measuring module comprises a distance measuring module and a phase measuring module;
the distance measurement module is used for extracting the distance from the user in the associated measurement information to any array element in the antenna array;
the phase measurement module is used for extracting the arrival phase from the user in the associated measurement information to each array element in the antenna array.
The measurement module further comprises an auxiliary measurement module, and the auxiliary measurement module is used for measuring auxiliary positioning information.
Specifically, as shown in fig. 1, the positioning base station includes: the antenna array is used for receiving a positioning signal transmitted by a user terminal; the clock synchronization module is used for synchronizing the clocks of all array elements of the antenna array; the measuring module is used for extracting measuring information related to the position of the user from signals received by the antenna array, wherein the measuring information comprises the distance from the user to any array element in the antenna array and the arrival phase from the user to each array element in the antenna array. Optionally, measuring unambiguous auxiliary positioning information, such as received signal strength, signal arrival time, etc.; the soft position information extraction module is used for extracting soft position information from the measurement information related to the position of the user; the communication module is used for sending the positioning result to the user terminal and receiving the inertial data from the user terminal; the positioning module is used for estimating the position of the user according to the extracted soft position information and the inertial data of the user terminal.
Optionally, the communication module is further configured to receive inertial data from the user terminal, and the positioning module is further configured to estimate the user position according to the inertial data of the user terminal.
Here, if the distance between two antennas is larger than half wavelength, the phase measurement is blurred.
Based on any one of the above embodiments, the user terminal includes a wireless measurement module, a terminal communication module, and an inertial measurement unit;
the wireless measurement module is used for transmitting the positioning signal to the positioning base station and receiving the positioning signal transmitted by the positioning base station;
the terminal communication module is used for receiving the positioning result sent by the positioning base station and sending inertial data to the positioning base station;
the inertia measurement unit is used for measuring the motion state of the user terminal.
Specifically, as shown in fig. 1, the user terminal includes:
the wireless measurement module is used for transmitting a positioning signal to the positioning base station and receiving the positioning signal transmitted by the positioning base station; the communication module is used for receiving the positioning result from the positioning base station.
Optionally, the user terminal further comprises an inertial measurement unit, wherein the inertial measurement unit is configured to measure a motion state of the user terminal, so as to improve positioning accuracy; correspondingly, the communication module is also used for sending inertia data to the base station.
Based on any of the above embodiments, fig. 2 is a schematic flowchart of a positioning method based on single base station soft location information provided by the present invention, and as shown in fig. 2, the method includes:
s1, acquiring an original positioning signal, and extracting associated measurement information from the original positioning signal;
s2, approximating the soft position information extracted from the correlation measurement information based on a Gaussian mixture model;
s3, estimating the user position based on the soft position information approximated by the Gaussian mixture model.
Specifically, the invention provides a positioning method based on single-base-station soft position information, which utilizes distance estimation and fuzzy phase measurement values, and is assisted by other non-fuzzy measurement information related to the position of a user to realize high-precision positioning. The positioning method extracts soft position information from a single base station raw measurement value, approximates the soft position information by utilizing a Gaussian mixture model to reserve the information of fuzzy positions brought by fuzzy phases, and further provides a Bayesian filtering algorithm based on the Gaussian mixture model to estimate the position of a user by utilizing the extracted soft position information and inertial sensor measurement.
The positioning method based on the single-base-station soft position information can effectively solve the problem of phase ambiguity caused by the fact that the antenna distance is longer than the half wavelength, breaks through the limitation that the antenna distance in the traditional positioning system based on phase measurement needs to be smaller than the half wavelength, and has no special requirements on array element arrangement of an antenna array, for example, the traditional direction finding algorithm generally requires that the array elements of the antenna array are arranged linearly or uniformly circularly. By utilizing the algorithm provided by the invention, the coupling effect among the antennas can be reduced by increasing the array element spacing of the antenna array so as to improve the quality of the received signal, and the accuracy of the signal incident angle (AOA) estimation algorithm can be obviously improved. By combining with the distance measurement, the large-scale high-precision positioning can be realized by using a single base station, the number of the required base stations and the system deployment cost are greatly reduced, and the positioning precision and the stability are improved.
Based on any of the above embodiments, step S2 in the method specifically includes:
extracting a fuzzy phase measurement result from the associated measurement information, and constructing a fuzzy likelihood function based on the fuzzy phase measurement result;
selecting a non-fuzzy measurement result from the associated measurement information, and constructing a non-fuzzy likelihood function based on the non-fuzzy measurement result;
estimating a mean value of each component based on the fuzzy likelihood function and the unambiguous likelihood function;
carrying out random sampling near the mean value of each component, and estimating a covariance matrix of the corresponding component;
and calculating the weight of each component based on the covariance matrix, and eliminating low-weight components to obtain the soft position information.
Specifically, as shown in fig. 3, the process of extracting soft location information is characterized by approximating the soft location information with a gaussian mixture model, comprising the steps of:
constructing a blurred likelihood function from the blurred phase measurements; selecting an unambiguous measurement from measurement results related to the position of the user to construct an unambiguous likelihood function; estimating the mean value of each component of the Gaussian mixture model; estimating a covariance matrix of corresponding components by randomly sampling near the mean value of each component of the Gaussian mixture model; and calculating the weight of each component, and eliminating the low-weight components.
Based on any of the above embodiments, step S3 in the method specifically includes:
defining state variables related to the user position, and constructing a state prediction equation and a state updating equation based on the state variables related to the user position;
calculating a probability distribution of the user position-dependent state variables based on the state prediction equation in combination with inertial data;
updating the probability distribution of the user location related state variables based on the state update equation in combination with the soft location information;
and repeating the state prediction and state updating process to obtain the user position estimation result.
Wherein said calculating a probability distribution of said user position dependent state variables based on said state prediction equation in combination with inertial data further comprises:
estimating the variation of the position of the user by combining the inertial data with a pedestrian dead reckoning algorithm;
or, the inertial data is combined with an inertial navigation algorithm to estimate the variation of the speed of the user.
Specifically, as shown in fig. 4, the proposed process of estimating a position is characterized by approximating a probability distribution of a state by using a gaussian mixture model, and comprises the following steps:
defining state variables related to the user position, and constructing a state prediction equation and a state updating equation; calculating the probability distribution of the user state variables according to a state prediction equation and by combining inertial data; updating the probability distribution of the user state variables according to a state updating equation and the soft position information; the state prediction and state update processes are repeated.
Alternatively, the amount of change in the user's position is estimated using inertial data in conjunction with a pedestrian dead reckoning algorithm or the amount of change in the user's speed is estimated using inertial data in conjunction with an inertial navigation algorithm.
Based on any of the above embodiments, the positioning method proposed by the present invention is described with specific embodiments.
The positioning method provided by the invention comprises the following steps: estimating the distance from a user to any array element in the antenna array, the arrival phase from the user to each array element in the antenna array and unambiguous auxiliary positioning information from signals originally received by the antenna array; extracting soft location information based on the measured information related to the user location; the user position is estimated from the extracted soft position information and inertial sensor data of the user terminal.
The first step is to estimate the distance from the user to any array element in the antenna array, the arrival phase from the user to each array element in the antenna array, and the auxiliary positioning information without ambiguity from the signal originally received by the antenna array. Setting the number of array elements of the antenna array as M, wherein the number of the required array elements is at least 3 for a two-dimensional plane positioning scene, and all the array elements are not collinear; for a three-dimensional positioning scene, the required array element number is at least 4, and the position of the ith antenna is set as ai＝[aix,aiy,aiz]. Let the location of the user be p ═ px,py,pz]TWhen two-dimensionally positioned, pzIs fixed as the coordinates of the plane in which the user is located. The distance measurement module is used for estimating the distance from a user to any array element in the antenna array, and without loss of generality, the distance measurement is assumed to be carried out between the user terminal and the antenna No. 1 of the antenna array, and the measurement result can be expressed as
Figure BDA0002886460850000111
Wherein
Figure BDA0002886460850000112
The distance from the user to the ith antenna.
Figure BDA0002886460850000113
The noise is measured for distance and conforms to a gaussian distribution. The distance measurement can be estimated through the received signal strength of signals such as WiFi, Bluetooth and the like, and can also be obtained through the signal arrival time of an ultra-wideband signal.
The phase measurement module is used to estimate the arrival phase of each array element in the antenna array, which can be expressed as
Figure BDA0002886460850000114
Wherein f iscIs the carrier frequency, c is the speed of light,
Figure BDA0002886460850000115
the noise measured for the signal arrival phase at the ith antenna conforms to a gaussian distribution. Delta thetaiFor the phase deviation between the ith antenna and the transmitting end of the receiving end, the clock synchronization module of the positioning base station can synchronize the clock signals among all the antennas, so that the delta theta of each antenna of the positioning base stationiAre all equal. Therefore, the phase deviation can be further eliminated by making a difference, and without loss of generality, the phase difference measurement result can be expressed by taking the antenna No. 1 of the antenna array as a reference antenna
Figure BDA0002886460850000116
Wherein C [ -1 [ ]M-1,IM-1]Is a transformation matrix. The noise of the phase difference measurement can be expressed as
Figure BDA0002886460850000117
The phase difference measurement can be obtained by ultra-wideband carrier phase measurement, WiFi Channel State Information (CSI) measurement, fixed frequency extended signal (CTE) in bluetooth 5.1, and the like. When the array element spacing of the antenna array is longer than half wavelength, the phase difference measurement result is limited to be-180 degrees, and can be expressed as
Figure BDA0002886460850000118
Wherein
Figure BDA0002886460850000119
Is a blurred phase difference measurement between-180 degrees and 180 degrees.
In addition to distance and phase measurements, the antenna array may also help eliminate phase ambiguity and improve positioning accuracy by measuring auxiliary positioning information, including measuring received signal strength using WiFi and bluetooth and measuring signal time difference of arrival using ultra-wideband. The received signal strength may be expressed as
Figure BDA0002886460850000121
Wherein the RSS0Signal strength at 1 meter of transmitting end; n is a signal attenuation coefficient;
Figure BDA0002886460850000122
the measured noise, which is the received signal strength, conforms to a gaussian distribution. The signal time difference of arrival measurement can be expressed as
τ＝C[t1,t2,…,tM]T,
Figure BDA0002886460850000123
Figure BDA0002886460850000124
Where diag (-) means that a diagonal matrix is generated from the individual elements of the vector, Δ tiFor the clock deviation between the ith antenna and the transmitting end of the receiving end, the clock synchronization module of the positioning base station can synchronize the clock signals between the antennas, so that the delta t of each antenna of the positioning base stationiAre all equal.
Figure BDA0002886460850000125
The noise measured for the signal arrival time at the ith antenna conforms to a gaussian distribution.
The second step is to extract soft position information according to the measured information related to the user position, and specifically comprises the following steps: constructing a blurred likelihood function from the blurred phase measurements; selecting an unambiguous measurement from measurement results related to the position of the user to construct an unambiguous likelihood function; estimating the mean value of each component of the Gaussian mixture model; estimating a covariance matrix of corresponding components by randomly sampling near the mean value of each component of the Gaussian mixture model; and calculating the weight of each component, and eliminating the low-weight components.
First, a fuzzy likelihood function is constructed from fuzzy phase measurement results
Figure BDA0002886460850000126
Where s represents the degree of ambiguity. Alternatively, when the noise of the phase difference measurement is assumed to follow a gaussian distribution, the fuzzy likelihood function can be written as
Figure BDA0002886460850000127
Wherein
Figure BDA0002886460850000128
For a blurred phase difference measurement, when a blur degree s is given, it can be converted into a blur-free absolute phase difference.
Next, unambiguous measures are selected from the measurements relating to the position of the user to construct an unambiguous likelihood function, including distance measures. When the noise of the distance measurement is assumed to fit into a gaussian distribution, its likelihood function can be written as
Figure BDA0002886460850000131
Wherein
Figure BDA0002886460850000132
Is a distance measurement. Alternatively, the likelihood function of the received signal strength in the auxiliary positioning information can be written as
Figure BDA0002886460850000133
Wherein the RSSiIs the signal strength of the ith antenna. The likelihood function of the signal arrival time difference in the auxiliary positioning information can be written as
Figure BDA0002886460850000134
Wherein
Figure BDA0002886460850000135
Is a measure of the time difference of arrival of the signals. Unambiguous likelihood function constructed from measurements relating to user position
Figure BDA0002886460850000136
The likelihood function of the range measurement is multiplied by the likelihood functions of all the auxiliary positioning information.
The soft position information obtained from the measurement values can be expressed as
Figure BDA0002886460850000137
Because of the ambiguity s, the soft position information is a multi-peak likelihood function, as shown in fig. 5, so we approximate the soft position information by a gaussian mixture model:
Figure BDA0002886460850000138
wherein beta isl,
Figure BDA0002886460850000139
ΣlRespectively, the weight, mean, and covariance matrix of the ith ambiguity. First, the mean of the gaussian components is estimated, and two methods are provided. One is by traversing all possible ambiguities s. Here, take the ith antenna as an example (i ≠ 1), possibly siIs taken as
Figure BDA00028864608500001310
Wherein
Figure BDA0002886460850000141
And
Figure BDA0002886460850000142
respectively representing a round-up and a round-down. For a certain possible ambiguity slThe mean value of the Gaussian components can be used for solving the following optimization problem by a gradient ascent method
Figure BDA0002886460850000143
Another approach is to search the local maxima of the fuzzy likelihood function in full space, i.e.
Figure BDA0002886460850000144
Alternatively, this problem can be solved by means of a lattice search. When the number of antennas is small and the distance between the antennas is small, the calculation complexity of the method 1 is low; in contrast, method 2 would be less computationally complex.
After the mean value of each Gaussian component is estimated, the covariance matrix of the corresponding component is estimated by randomly sampling near the mean value of each component of the Gaussian mixture model. For a certain Gaussian component l, we mean it
Figure BDA0002886460850000145
Nearby random generation of NsIndividual sample points, the covariance matrix is estimated by
Figure BDA0002886460850000146
Figure BDA0002886460850000147
Wherein. varies indicates that p is proportional tol,iTo represent
Figure BDA0002886460850000148
The ith sample point nearby. Then, the weight of each Gaussian component is
Figure BDA0002886460850000149
Where det {. cndot } represents a determinant of the matrix. Finally, the lower weight component is removed.
The third step is to estimate the user position based on the extracted soft position information and inertial data of the user terminal. The positioning problem can be expressed as a series of different discrete-time variables (x)k,mk,yk) Wherein x iskIndicates the state of the user at the k-th moment, including the user's location pkOptionally, the moving speed of the user may be included
Figure BDA00028864608500001410
mkRepresenting the measurement of the inertial sensor at time k. Alternatively, when used for pedestrian positioning, the inertial sensor data may be used to estimate the pedestrian's walking direction and step size using a pedestrian dead reckoning algorithm, the result of which is mkContaining the user's displacement; when the method is used for non-pedestrian positioning, such as unmanned aerial vehicles, unmanned vehicles and the like, the inertial sensor data can be used for estimating the speed change of equipment by using an inertial navigation algorithm, and the result m iskIncluding changes in device speed; when the user terminal is not equipped with the inertial sensing unit, m can be simply omittedk。ykIndicating the measurement results of positioning the base station at the k-th time. The positioning algorithm estimates the position of the user terminal through the measurement result of the positioning base station and the data of the inertial sensing unit on the user terminal, and can be expressed as a state prediction equation and a state update equation:
xk＝f(xk-1,mk,wk)
yk＝hk(xk,vk),
wherein wkAnd vkProcess noise and observation noise, respectively. Based on the system model, the Bayesian filtering process can be divided into a state prediction process and a state updating process, which are respectively as follows:
p(xk|y1:k-1)∝∫p(xk|xk-1,mk)p(xk-1|y1:k-1)dxk-1
p(xk|y1:k)∝p(yk|xk)p(xk|y1:k-1)，
wherein
Figure BDA0002886460850000151
For the soft position information estimated in the second step, a gaussian mixture model is approximated.
In one embodiment of the invention, we assume that the user's state contains the user's location and the user's moving speed, i.e. the user's moving speed
Figure BDA0002886460850000152
The state prediction equation is expressed as
xk＝Axk-1+Gkmk+wk，
Wherein
Figure BDA0002886460850000153
At is the time interval between two time instants,
Figure BDA0002886460850000154
is process noise. The system observation model is soft position information extracted in the second step and is approximate to a Gaussian mixture model:
Figure BDA0002886460850000155
wherein
H＝[I3×3,03×3]。
Alternatively, when the user terminal is equipped with an inertial sensing unit, the change in the moving speed of the user terminal can be estimated using an inertial navigation algorithm, when
Figure BDA0002886460850000161
Gk＝[0.5ΔtI3×3,I3×3]，
Wherein
Figure BDA0002886460850000162
For calculated speed change, sigmav,kThe covariance matrix of its noise. When the user terminal is provided with the inertial sensing unit, the pedestrian travel direction and the step length are detected by utilizing a pedestrian dead reckoning algorithm
Figure BDA0002886460850000163
Figure BDA0002886460850000164
Gk＝I6×6，
Wherein SLkStep size of step k, θkThe walking direction of the k step. Omitting G in the state prediction model when the user terminal is not equipped with an inertial sensing unitkmkThe item is just needed.
Approximating a probability distribution of state variables to a Gaussian mixture model
Figure BDA0002886460850000165
And substituting the state prediction process into a Bayesian filtering formula
Figure BDA0002886460850000166
Figure BDA0002886460850000167
Figure BDA0002886460850000168
The status update process is
Figure BDA0002886460850000169
Figure BDA00028864608500001610
Figure BDA00028864608500001611
Figure BDA00028864608500001612
Figure BDA00028864608500001613
j＝(i-1)Lk+l
Nk|k＝Nk|k-1Lk
Figure BDA00028864608500001614
The positioning method provided by the invention can effectively solve the problem of phase ambiguity caused by the fact that the distance between the array antennas is larger than half wavelength in the positioning of the single base station, thereby obviously improving the positioning accuracy by increasing the aperture of the antenna array.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A positioning system based on single base station soft location information, comprising: positioning a base station and a user terminal;
the positioning base station is used for receiving positioning data sent by the user terminal and generating a positioning result based on the positioning data;
and the user terminal is used for sending the positioning data to the positioning base station and receiving the positioning result.
2. The single base station soft position information based positioning system according to claim 1, wherein the positioning base station comprises an antenna array, a clock synchronization module, a positioning module, a base station communication module, a measurement module and a soft position information extraction module;
the antenna array is used for receiving a positioning signal transmitted by the user terminal;
the clock synchronization module is used for synchronizing clocks of all array elements in the antenna array;
the base station communication module is used for sending the positioning result to the user terminal;
the measurement module is used for extracting the associated measurement information of the user position from the signals received by the antenna array;
the soft position information extraction module is used for extracting soft position information from the associated measurement information;
the positioning module is configured to estimate a user position based on the extracted soft location information.
3. The single base station soft position information based positioning system of claim 2, wherein the base station communication module is further configured to receive inertial data from the user terminal;
accordingly, the location module is further configured to estimate the user location from the inertial data.
4. The single base station soft position information based positioning system of claim 2, wherein the measurement module comprises a distance measurement module and a phase measurement module;
the distance measurement module is used for extracting the distance from the user in the associated measurement information to any array element in the antenna array;
the phase measurement module is used for extracting the arrival phase from the user in the associated measurement information to each array element in the antenna array.
5. The single base station soft position information based positioning system of claim 4, wherein the measurement module further comprises an auxiliary measurement module for measuring auxiliary positioning information.
6. The single base station soft position information based positioning system of claim 1, wherein the user terminal comprises a wireless measurement module, a terminal communication module and an inertial measurement unit;
the wireless measurement module is used for transmitting the positioning signal to the positioning base station and receiving the positioning signal transmitted by the positioning base station;
the terminal communication module is used for receiving the positioning result sent by the positioning base station and sending inertial data to the positioning base station;
the inertia measurement unit is used for measuring the motion state of the user terminal.
7. A positioning method based on the positioning system of any claim 1 to 6, characterized in that it comprises:
acquiring an original positioning signal, and extracting associated measurement information from the original positioning signal;
approximating soft location information extracted from the associated measurement information based on a Gaussian mixture model;
estimating a user location based on the soft location information approximated by the Gaussian mixture model.
8. The method according to claim 7, wherein the approximating the soft location information extracted from the correlation measurement information based on the gaussian mixture model specifically comprises:
extracting a fuzzy phase measurement result from the associated measurement information, and constructing a fuzzy likelihood function based on the fuzzy phase measurement result;
selecting a non-fuzzy measurement result from the associated measurement information, and constructing a non-fuzzy likelihood function based on the non-fuzzy measurement result;
estimating a mean value of each component based on the fuzzy likelihood function and the unambiguous likelihood function;
carrying out random sampling near the mean value of each component, and estimating a covariance matrix of the corresponding component;
and calculating the weight of each component based on the covariance matrix, and eliminating low-weight components to obtain the soft position information.
9. The method according to claim 7, wherein estimating the user location based on the soft location information approximated by the gaussian mixture model comprises:
defining state variables related to the user position, and constructing a state prediction equation and a state updating equation based on the state variables related to the user position;
calculating a probability distribution of the user position-dependent state variables based on the state prediction equation in combination with inertial data;
updating the probability distribution of the user location related state variables based on the state update equation in combination with the soft location information;
and repeating the state prediction and state updating process to obtain the user position estimation result.
10. The method of claim 9, wherein said calculating a probability distribution of said user position-dependent state variables based on said state prediction equation in combination with inertial data further comprises:
estimating the variation of the position of the user by combining the inertial data with a pedestrian dead reckoning algorithm;
or, the inertial data is combined with an inertial navigation algorithm to estimate the variation of the speed of the user.
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