CN113514797B - Automatic calibration method of UWB base station - Google Patents

Automatic calibration method of UWB base station Download PDF

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Publication number
CN113514797B
CN113514797B CN202110778716.4A CN202110778716A CN113514797B CN 113514797 B CN113514797 B CN 113514797B CN 202110778716 A CN202110778716 A CN 202110778716A CN 113514797 B CN113514797 B CN 113514797B
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base station
matrix
uwb
automatic calibration
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CN113514797A (en
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王安成
欧阳文
李建胜
张伦东
马嘉琳
郭雨岩
汲振
李凯林
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Information Engineering University of PLA Strategic Support Force
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0294—Trajectory determination or predictive filtering, e.g. target tracking or Kalman filtering
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The invention relates to an automatic calibration method of a UWB base station, and belongs to the technical field of base station calibration. The automatic calibration method comprises the following steps: constructing a base station position estimation model based on Kalman filtering or extended Kalman filtering, wherein the base station position estimation model comprises a state equation and a measurement equation of a base station position; in the motion process of the motion carrier, acquiring outdoor positioning information of the motion carrier and distance information between the motion carrier and each base station at corresponding moment in real time; and (3) inputting the positioning information of the motion carrier and the distance information between the motion carrier and each base station at the corresponding moment into a base station position estimation model, and obtaining the position information of each base station through time updating and measurement updating to finish the calibration of each base station. According to the invention, the automatic calibration of the UWB base station is realized by using the outdoor positioning information and the base station position estimation model, so that the automatic calibration efficiency of the UWB base station is improved, and the unification of the outdoor coordinate reference and the UWB coordinate reference is realized.

Description

Automatic calibration method of UWB base station
Technical Field
The invention relates to an automatic calibration method of a UWB base station, and belongs to the technical field of base station calibration.
Background
The indoor and outdoor seamless positioning technology refers to a technology for positioning people or other carriers in real time, continuously and reliably by adopting different positioning methods in a combined way in the indoor closed and outdoor open space range in which human activities are concentrated. The realization of indoor and outdoor seamless positioning is a key support for expanding the position service, and has great application value.
In outdoor open space, positioning can be conveniently and reliably realized by means of a global navigation satellite system (Global Navigation Satellite System, GNSS), but GNSS generally cannot provide positioning service due to weak signals in places where buildings are dense, indoor, underground and the like are blocked. In order to solve the problem of positioning indoor space, students at home and abroad sequentially put forward an autonomous positioning method based on sensors such as inertia and vision and a base station positioning method based on radio beacons such as WiFi, bluetooth, ultra-wideband (UWB), and the like, and in the methods, UWB positioning technology has the advantages of high precision, good multipath resistance and the like, and is paid attention to widely. Therefore, a learner can realize indoor and outdoor seamless positioning by combining the GNSS and UWB technologies, but the problem of seamless conversion of position information between two coordinate systems needs to be solved because the GNSS coordinate reference used outdoors is inconsistent with the coordinate reference used for indoor UWB positioning.
In a UWB positioning system, a certain number of UWB base stations are usually deployed in advance, and the position of the tag to be positioned is calculated by combining the position of the base station with the distance information obtained by real-time measurement between the tag to be positioned and the base station. The process of determining the location of a base station itself is generally referred to as calibration of the base station, which is a precondition for achieving UWB positioning.
In general, UWB base station calibration is achieved by manual measurement using a ruler, a range finder, or the like in advance, which is time-consuming and laborious. In order to improve the deployment efficiency, a learner also proposes a method for realizing automatic calibration by combining the ranging function of the UWB with a certain rule, and the method generally requires to specify the basic layout of UWB placement and a base station positioned at the origin of coordinates in advance, and still has the defects of low applicability and low efficiency.
Disclosure of Invention
The purpose of the application is to provide an automatic calibration method of a UWB base station, which is used for solving the problems of poor applicability and low efficiency of the existing calibration mode.
In order to achieve the above purpose, the present application proposes a technical solution of an automatic calibration method of a UWB base station, the automatic calibration method comprising the following steps:
1) Constructing a base station position estimation model based on Kalman filtering or extended Kalman filtering, wherein the base station position estimation model comprises a state equation and a measurement equation of a base station position;
2) In the motion process of the motion carrier, acquiring outdoor positioning information of the motion carrier and distance information between the motion carrier and each base station at corresponding moment in real time;
3) And (3) inputting the positioning information of the motion carrier and the distance information between the motion carrier and each base station at the corresponding moment into a base station position estimation model, and obtaining the position information of each base station through time updating and measurement updating to finish the calibration of each base station.
The technical scheme of the automatic calibration method of the UWB base station has the advantages that: the invention fully considers the characteristics of indoor and outdoor seamless positioning application, realizes the automatic calibration of the UWB base station by utilizing the outdoor positioning information and the base station position estimation model, not only improves the automatic calibration efficiency of the UWB base station, but also realizes the unification of the outdoor coordinate reference and the UWB coordinate reference, and solves the problem of seamless conversion between the indoor coordinate system and the outdoor coordinate system.
Further, in the step 2), the outdoor positioning information is GNSS positioning information.
Further, in order to improve the calibration efficiency, the state equation in the step 1) is a state equation using the plane position of each base station as a state quantity, and the step 3) further includes a step of converting the outdoor positioning information into a plane coordinate system before the step 3), and the step 3) further includes a step of converting the obtained position information into a geodetic coordinate system.
Further, the outdoor positioning information is converted into a planar coordinate system by means of UTM projection transformation.
Further, the base station position estimation model is constructed according to the extended kalman filter, and the state equation is:
X(k)＝Φ(k/k-1)X(k-1)+Γ(k-1)W(k-1)，
wherein X (k) is a base station state matrix at the kth moment; x (k-1) is a base station state matrix at the k-1 time; phi (k/k-1) is a state transition matrix from the kth moment to the kth moment; Γ (k-1) is the noise drive matrix at time k-1; w (k-1) is the process noise matrix at time k-1.
Further, the state transition matrix is an identity matrix.
Further, the noise driving matrix is a diagonal matrix.
Further, the measurement equation is obtained according to the observation matrix of each base station.
Further, the measurement equation in the measurement update process is:
ΔZ(k)＝H(k)△X(k)+R(k)；
wherein Δz (k) is the observed quantity deviation matrix at the kth time; deltaX (k) is all base station state deviation matrixes at the kth moment; r (k) is an observation noise matrix at the kth moment; h (k) is the jacobian matrix of all base stations at time k.
Further, add d in the measurement updating process i (k) And H i (k)，d i (k) Is the theoretical distance value H between the ith base station at the kth moment and the motion carrier i (k) The Jack ratio is the Jack ratio of the ith base station at the kth moment.
Drawings
FIG. 1 is a flow chart of an automatic calibration method of a UWB base station of the present invention;
fig. 2 is a schematic filtering diagram of an EKF base station position estimation model according to the present invention.
Detailed Description
Automatic calibration method embodiment of UWB base station:
the invention mainly aims at solving the problems of poor calibration applicability and low efficiency of the existing UWB base station, constructs a base station position estimation model according to a Kalman filtering or extended Kalman filtering principle, inputs the outdoor positioning information of a motion carrier and the distance information between the motion carrier and each base station into the base station position estimation model to obtain the position information of each base station, realizes the automatic calibration of each base station, improves the calibration efficiency, and realizes the unification of an outdoor coordinate reference and a UWB coordinate reference.
Specifically, the automatic calibration method of the UWB base station is shown in fig. 1, and comprises the following steps:
1) And constructing an EKF base station position estimation model according to the extended Kalman filtering principle.
The EKF base station position estimation model includes a state equation and a measurement equation for the base station plane position.
The determination of the state equation is as follows:
the plane position of each base station is selected as a state quantity, and a base station state matrix X= [ X ] is assumed 1 X 2 …X i … X m ] T The method comprises the steps of carrying out a first treatment on the surface of the Wherein X is i ＝[x i y i ]，X i And the plane coordinate of the ith base station is equal to or more than 1 and equal to or less than m, and m is the number of the base stations.
Considering that the base station remains stationary during the process, the state transition matrix Φ is an identity matrix, and the noise driving matrix Γ is a diagonal matrix, so the state equation is: x (k) =Φ (k/k-1) X (k-1) +Γ (k-1) W (k-1),
wherein X (k) is a base station state matrix at the kth moment; x (k-1) is a base station state matrix at the k-1 time; phi (k/k-1) is a state transition matrix from the kth moment to the kth moment; Γ (k-1) is the noise drive matrix at time k-1; w (k-1) is the process noise matrix at time k-1.
In the above state equation, the specific form of the state transition matrix Φ and the noise driving matrix Γ is as follows:
wherein T is the update period of the state equation.
And, the root mean square matrix Q of the process noise matrix W is:
wherein w is an adjustable parameter, w < 1.
The measurement equation is determined as follows:
selecting a distance value from a motion carrier to a base station as an observed quantity, and assuming an observed matrix Z= [ Z ] 1 Z 2 …Z i … Z m ] T ；
Wherein Z is i The observation matrix from the ith base station to the motion carrier comprises the observation quantity from the ith base station to the motion carrier at each moment, and an observation equation of the ith base station can be obtained according to a distance formula of two points on a plane: z is Z i (k)＝||X s -X i || 2 +R (k); wherein Z is i (k) The observed quantity from the ith base station to the motion carrier at the kth moment; x is X s Plane coordinates [ x ] for moving carrier s y s ]；X i The plane coordinates of the ith base station are R (k) is observation noise at the kth moment; i 2 Representing the two norms of the vector.
And linearizing and differentiating the observation equation to obtain a jacobian matrix H, wherein the process is as follows:
will Z i (k)＝||X s -X i || 2 The +R (k) expansion can be obtained:
at the same time let->Further linearizing to obtain jacobian H of the ith base station at the kth moment i (k) The following are provided:
observation matrix Z linking base stations to moving carrier 1 ～Z m The measurement equation of the model in the measurement updating process is obtained as follows:
ΔZ(k)＝H(k)△X(k)+R(k)；
wherein Δz (k) is the observed quantity deviation matrix at the kth time; deltaX (k) is all base station state deviation matrixes at the kth moment; r (k) is an observation noise matrix at the kth moment; h (k) is the jacobian matrix of all base stations at time k.
2) The GNSS receiver and UWB tag are mounted on a moving carrier, which may be a person or other moving body.
In the step, the GNSS receiver can output positioning information with the output frequency of 1-10Hz, and the UWB tag can measure the distance information between the GNSS receiver and each base station to be calibrated with the frequency of 1-10Hz.
3) The motion carrier moves in a set range, and in the motion process, positioning data (namely, outdoor positioning information is GNSS positioning information) output by the GNSS receiver and distances (namely, distance information) from the UWB tag to each base station to be calibrated are synchronously acquired.
The setting range in the step refers to a space which is in an outdoor open area and can ensure the normal communication distance between the base station to be calibrated, and whether the base station to be calibrated is indoor or not is not required, so that the outdoor positioning data and the distance data can be obtained.
4) And (3) carrying out coordinate conversion on the positioning data obtained in the step (3) by adopting a UTM projection conversion mode to obtain UTM plane coordinates.
In this step, the positioning data output by the GNSS receiver is data in the geodetic coordinate system, including longitude and latitude, but the EKF base station position estimation model is the plane coordinates of the base station, so in order to unify the GNSS coordinates and the base station coordinates, it is necessary to perform coordinate conversion on the positioning data output by the GNSS receiver and convert the positioning data into the plane coordinates.
In this embodiment, UTM projective transformation is used to obtain UTM plane coordinates, and as other embodiments, gaussian projective transformation may be used to obtain gaussian plane coordinates.
Specifically, the procedure of UTM projective transformation is as follows:
γ＝λ-λ 0 ；
wherein x is the value of the x axis under UTM plane coordinates; y is the value of the y axis under UTM plane coordinates; λ is the longitude in the geodetic coordinate system;is the latitude under the geodetic coordinate system; a is a long half shaft of which the earth approximates to ellipse; b is a short half shaft of which the earth approximates to ellipse; e is the eccentricity; 0.9996 is the length ratio of the central meridian; lambda (lambda) 0 The starting longitude of the UTM projection belt where the mobile station coordinates (i.e., the motion carrier coordinates) are located.
5) And (3) inputting the plane positioning data and the distance data converted in the step (4) into an EKF base station position estimation model, obtaining UTM plane positions of all the base stations through time updating and measurement updating, and further converting UTM plane coordinates of all the base stations into a geodetic coordinate system to obtain the positions of all the base stations in the geodetic coordinate system.
A specific calculation of the EKF base station position estimate is shown in FIG. 2, where X 0 For initial value of state quantity, P 0 For covariance initial values, X (K/K-1) is a prediction matrix of which the state variable changes from K-1 time to K time, P (K/K-1) is a time update process covariance matrix from K-1 time to K time, P (K) is a time measurement update process covariance matrix, K (K) is a filter gain matrix at K time, Q (K) is a state noise matrix, R (K) is an observation noise matrix, and d (K) is d i (k) Representing a theoretical value of the base station to mobile station distance during observation; the model divides the whole filtering process into two stages of time updating and measurement updating, and d is added in the measurement updating stage compared with the traditional Kalman filtering updating model i (k) And H i (k) After that, the model can meet the filter estimation of any base station, and the number of the base stations can be increased or decreased in real time, thereby being beneficial to improving the calibration efficiency, wherein d i (k) Combining into d (k) the distance needed for measurement, H i (k) The distance quantity of a single base station and the coordinates of a mobile station are determined, the base station and the mobile station are combined into a jacobian matrix H (k), and the processes are closely combined and orderly carried out.
In the above embodiment, in order to improve the calibration efficiency, the positioning data is down-converted from the geodetic coordinate system to the planar coordinate system in the calibration process, and as other embodiments, the calibration may be directly performed in the geodetic coordinate system without considering the calibration efficiency.
In the above embodiment, in order to perform base station calibration more accurately, the adopted model is an EKF base station position estimation model, and as other embodiments, the base station calibration may also be performed by adopting a base station position estimation model constructed by a traditional kalman filter, which is not limited in the present invention.
In the above embodiment, the outdoor positioning information is obtained by using a GNSS receiver, and as other embodiments, the outdoor positioning information may also be obtained by using a GPS receiver.
The invention fully considers the characteristics of indoor and outdoor seamless positioning application, and provides a technical scheme for realizing automatic calibration of the UWB base station by utilizing the outdoor positioning information, so that the problem of automatic calibration of the UWB base station can be effectively solved, and the unification of an outdoor coordinate reference and a UWB coordinate reference is realized.

Claims (6)

1. An automatic calibration method of a UWB base station is characterized by comprising the following steps:
1) Constructing a base station position estimation model based on extended Kalman filtering, wherein the base station position estimation model comprises a state equation and a measurement equation of a base station position;
2) In the motion process of the motion carrier, acquiring outdoor positioning information of the motion carrier and distance information between the motion carrier and each base station at corresponding moment in real time;
3) Inputting the positioning information of the motion carrier and the distance information between the motion carrier and each base station at the corresponding moment into a base station position estimation model, and obtaining the position information of each base station through time updating and measurement updating to finish the calibration of each base station;
the state equation in the step 1) is a state equation taking the plane position of each base station as a state quantity, the step 3) is preceded by a step of converting outdoor positioning information into a plane coordinate, and the step 3) also comprises a step of converting the obtained position information into a geodetic coordinate system;
the base station position estimation model is constructed according to the extended Kalman filtering, and the state equation is as follows:
X(k)＝Φ(k/k-1)X(k-1)+Γ(k-1)W(k-1)，
wherein X (k) is a base station state matrix at the kth moment; x (k-1) is a base station state matrix at the k-1 time; phi (k/k-1) is a state transition matrix from the kth moment to the kth moment; Γ (k-1) is the noise drive matrix at time k-1; w (k-1) is a process noise matrix at the k-1 time;
the measurement equation is obtained according to the observation matrix of each base station; the measurement equation in the measurement update process is:
ΔZ(k)＝H(k)ΔX(k)+R(k)；
wherein Δz (k) is the observed quantity deviation matrix at the kth time; Δx (k) is all base station state deviation matrices at the kth time; r (k) is an observation noise matrix at the kth moment; h (k) is the jacobian matrix of all base stations at time k.
2. The automatic calibration method of UWB base station according to claim 1, wherein the outdoor positioning information in the step 2) is GNSS positioning information.
3. The automatic calibration method of UWB base station of claim 1 wherein the outdoor positioning information is converted into a planar coordinate system by UTM projective transformation.
4. The automatic calibration method of a UWB base station of claim 1 wherein the state transition matrix is an identity matrix.
5. The automatic calibration method of a UWB base station of claim 1 wherein the noise driving matrix is a diagonal matrix.
6. The automatic calibration method of UWB base station according to claim 1, wherein d is added in the measurement update process i (k) And H i (k)，d i (k) Is the theoretical distance value H between the ith base station at the kth moment and the motion carrier i (k) The Jack ratio is the Jack ratio of the ith base station at the kth moment.
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