Distributed multi-station track drawing method oriented to cellular network high-precision seamless perception

Abstract

The invention provides a distributed multi-station track drawing method for high-precision seamless perception of a cellular network, and the method comprises the steps: 1, building a target motion model, calculating a measurement error boundary according to the signal-to-noise ratio of a base station, and carrying out the extended Kalman filtering of the measurement data of each base station, so as to obtain each local track; step 2, performing track initiation by using the private coverage area track auxiliary reference measurement of the original base station, and obtaining a reference track through extended Kalman filtering; step 3, carrying out flight path fusion on the flight paths of the overlapping coverage areas of the base stations to obtain high-precision flight paths of the overlapping coverage areas; updating the private coverage area track of the base station; and connecting the updated flight path of each base station private coverage area and the fused flight path of the overlapped coverage area to obtain the overall situation of the target flight path. By using the method, complete tracking of a cross-base-station target can be realized, the problems of track interruption and insufficient precision of multi-base-station time-sharing detection of a low-altitude target are solved, and the practicability and reliability of the system are greatly enhanced.

Description

Technical Field

[0001] The present invention belongs to the field of target tracking, and in particular relates to a distributed multi-station track drawing method for high-precision seamless perception of cellular networks. Background Art

[0002] In modern urban environments, drones and other micro-aircraft play an increasingly important role in logistics distribution, environmental monitoring, emergency response and other fields due to their fast, compact and highly maneuverable nature. They are of great significance to the development of the low-altitude economy. The challenge that follows is how to effectively detect and track these high-speed moving targets. Traditional single-base station tracking systems have many limitations. First, incomplete target tracking is a significant problem. Due to the limited coverage of a single base station, when the target flies out of the coverage area, tracking will be lost, which cannot meet the needs of large-scale monitoring, especially in complex environments such as cities, where buildings and other obstacles will greatly limit the propagation and reception of signals. In addition, different base stations often use different signal frequencies, which leads to different target data rates, making it difficult to effectively integrate and analyze. In the distributed multi-station detection and tracking scenario, the overlapping area is located at the marginal area of ​​the detection power of each base station, the target signal-to-noise ratio decreases, and the base station loses the measurement accuracy of micro-targets. In addition, the design of the communication base station itself takes into account the wide coverage characteristics, which makes its measurement accuracy limited, especially when using wide beam coverage, the angle measurement accuracy error is large. This will lead to difficulties in starting the track in the overlapping coverage area, and the filter convergence will take a long time, or even fail to converge in the entire overlapping area. Summary of the invention

[0003] Purpose of the invention: The technical problem to be solved by the present invention is to provide a distributed multi-station track drawing method for high-precision seamless perception of cellular networks in view of the shortcomings of the prior art. Based on the target observation data and error bounds of each base station, the problem of incomplete target track tracking across base stations in the city is solved.

[0004] The present invention comprises the following steps:

[0005] Step 1: Obtain the total number of base stations, base station measurement data and the initial state of the target, establish a target motion model, calculate the measurement error bound based on the signal-to-noise ratio of the base station, and perform extended Kalman filtering on the measurement data of each base station to obtain each local track;

[0006] Step 2: In a given overlapping coverage area, the tracks of the overlapping coverage areas of each base station are aligned in time and space and then associated. According to the track measurement values ​​and error bounds of the overlapping coverage areas of each base station that have been successfully associated, the reference measurement is obtained through maximum likelihood estimation. The track is started using the auxiliary reference measurement of the private coverage area track of the original base station, and the reference track is obtained through extended Kalman filtering.

[0007] Step 3, calculate the reference error between the reference track and the tracks of the overlapping coverage areas of each base station successfully associated in step 2, dynamically assign weights according to the error, fuse the tracks of the overlapping coverage areas of each base station, and obtain a high-precision track of the overlapping coverage area; use the fused track of the overlapping coverage area to assist the next base station in re-filtering, and update the track of the private coverage area of ​​the base station; connect the updated track of each base station's private coverage area with the fused track of the overlapping coverage area to obtain the overall situation of the target track.

[0008] Step 1 includes: establishing the following linear system: (1), (2), in express The state vector at time, express The moment measurement vector, express The state transfer matrix at time , express The measurement matrix of time, express The process noise distribution matrix at time , represents the process noise, represents the measurement noise, and All are zero-mean Gaussian white noise;

[0009] Step 1-2, establish the target motion model:

[0010] For the uniform linear motion model, the state vector describing the dynamic characteristics of the system ,symbol Indicates transposition; the state transition is as follows:

[0011] (3),

[0012] in express The goal at all times The position of the axis, express The goal at all times The position of the axis, express The goal at all times The speed of the axis, express The goal at all times The speed of the axis, Indicates the target Random variations in axis speed, Indicates the target Random variations in axis speed, represents the sampling interval;

[0013] Step 1-3, calculate the measurement error bound based on the signal-to-noise ratio of each base station:

[0014] Distance measurement error standard deviation for:

[0015] (4),

[0016] in represents the speed of light constant, represents the receiver bandwidth, Indicates the base station signal-to-noise ratio;

[0017] Azimuth measurement error standard deviation for:

[0018] (5),

[0019] in represents the antenna beamwidth for measuring azimuth, Indicates the single pulse slope;

[0020] Step 1-4, perform extended Kalman filtering on the measurements obtained by the base station.

[0021] Steps 1-4 include: the dynamic equation and measurement equation of the system satisfy the Gaussian condition, expressed as:

[0022] (6),

[0023] (7),

[0024] Among them, the nonlinear function and They represent the dynamic model function and the measurement model function, respectively, and the process noise and measurement noise All obey Gaussian distribution, and the covariance of process noise is , the covariance of the measurement noise is ;

[0025] The one-step prediction of the state is expressed as:

[0026] (8),

[0027] in for The state prediction at the moment, for Time to Status prediction at each moment;

[0028] The one-step forecast of the covariance is expressed as:

[0029] (9),

[0030] in for The state estimation covariance at time , for Time to State estimation covariance prediction at time instant; For vector The Jacobian matrix of for The transposed matrix of

[0031] Measuring predicted values , the covariance of the measured predicted value is expressed as:

[0032] (10),

[0033] in for time The Jacobian matrix of for Time to The measured predicted value at time, for The transposed matrix of for The information covariance matrix at each moment;

[0034] The gain is:

[0035] (11),

[0036] in for The filter gain at time t, for The inverse matrix of

[0037] The state update equation is:

[0038] (12),

[0039] in for Status updates at all times, for The measured value at the moment;

[0040] The covariance update equation is:

[0041] (13),

[0042] in is the identity matrix of the same dimension as the covariance, for The transposed matrix of

[0043] Repeat steps 1-4 until the status update is completed and the local track of each base station is obtained. ,in is the total number of base stations, It is a base station The number of tracks.

[0044] Step 2 includes:

[0045] Step 2-1: The local tracks of each base station obtained by filtering in step 1 are Transfer to a unified rectangular coordinate system through spatial alignment, and then perform time alignment by interpolation and extrapolation of overlapping tracks of each base station;

[0046] Step 2-2, weighted track association is performed on the tracks of the overlapping coverage areas of each base station after the time-space alignment in step 2-1:

[0047] set up , for two base stations, namely the first base station and the second base station, the following formula is used to test the statistic:

[0048] (14),

[0049] in express The first base station The estimated state of the target, express The second base station The estimated state of the target, express The first base station The error covariance of the target, express The second base station The error covariance of the targets; if Below using chi-square distribution The first base station is determined based on a certain threshold obtained. The target and the second base station The targets are the same target, namely the first base station Track and the second base station Track association;

[0050] Step 2-3, for the track measurement information of the overlapping coverage area of ​​each base station that is successfully associated, the reference measurement is calculated by maximum likelihood estimation according to the error bound:

[0051] (15),

[0052] in represents the likelihood function, represents the probability density function of the first track measurement value in the successfully associated track pair, represents the probability density function of the second track measurement value in the successfully associated track pair, where the probability density function of each measurement value is It is expressed as:

[0053] (16),

[0054] in Indicates the track The measured value is in the rectangular coordinate system The position on the axis, Indicates the track The measured value is in the rectangular coordinate system The position on the axis, Indicates the track The distance measurement error of the measured value in the polar coordinate system, Indicates the track The measurement error of the azimuth of the measured value in the polar coordinate system, Indicates the track The distance of the measured value in the polar coordinate system, Indicates the track The azimuth of the measured value in the polar coordinate system, Indicates the track The reference measurement is in rectangular coordinates. The position on the axis, Indicates the track The reference measurement is in rectangular coordinates. The position on the axis, Indicates the track The distance measured in polar coordinates is Indicates the track The azimuth of the reference measurement in the polar coordinate system; exp represents the natural exponential function;

[0055] Step 2-4: Use the method of step 1-4 to perform extended Kalman filtering on the reference measurement obtained in step 2-3 to obtain the target Reference track filter value (The target i here refers to the target i confirmed after the successful association of track i and track j, that is, two successfully associated tracks determine one target, and the reference measurement and reference track are also for this determined target).

[0056] Step 3 includes:

[0057] Step 3-1, calculate the reference error between the reference track and the track of the overlapping coverage area of ​​each base station successfully associated in step 2-2, and for the target determined by the successful track association , the formula is:

[0058] (17),

[0059] in express Time base station The detection The filtered value of the target, express Moment The filtered value of the target reference track, express Time base station No. The reference error between the target overlap area track and the reference track;

[0060] Step 3-2, target The reference error is normalized:

[0061] (18),

[0062] in express Time to When the state of each target is fused, the base station The weight of participation;

[0063] Step 3-3, dynamically weighted fusion of the tracks of the overlapping coverage areas of the base stations successfully associated in step 2-2, The inverse of the covariance matrix and the weighted accumulation of the state vector:

[0064] (19),

[0065] (20),

[0066] in, express The target detected by each base station at any time The weighted accumulation of the inverse of the track covariance matrix of the overlapping coverage area, express Time base station Detection target The inverse of the covariance matrix of express The target detected by each base station at any time The weighted accumulation of the track state vectors in the overlapping coverage area, express Time base station Detection target The overlapping coverage area track state vector;

[0067] Step 3-4, calculate the fused error covariance matrix and state estimation;

[0068] Step 3-5, if the target flies from the overlapping coverage area into the private coverage area of ​​a base station, the error covariance matrix and state estimation fused in step 3-4 are used to assist the base station in re-filtering and update the private coverage area track of the base station; the updated private coverage area track of each base station and the fused overlapping coverage area track are connected to obtain the overall situation of the target track.

[0069] In step 3-4, the following formula is used to calculate the fused error covariance matrix and state estimate:

[0070] (twenty one),

[0071] (twenty two),

[0072] in, express Time target The error covariance matrix after fusion, express Time target State estimation after fusion.

[0073] The present invention also provides an electronic device, comprising a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor executes the steps of the described method.

[0074] The present invention also provides a storage medium storing a computer program or instruction. When the computer program or instruction is run on a computer, the steps of the method described are executed.

[0075] Beneficial effects: The method of the present invention integrates the measurement data from multiple base stations, estimates the measurement error bound according to the base station signal-to-noise ratio, and obtains the local track and reference track of the target. The weight coefficients are dynamically allocated in the overlapping coverage area to perform weighted fusion on the track of the target overlapping coverage area, thereby achieving complete track drawing for cross-base station targets, which not only expands the spatiotemporal coverage of communication base station detection, but also improves tracking accuracy. Based on the advantages of 5G communication base stations and interawareness integration technology, the present invention proposes a distributed multi-station track drawing method for high-precision seamless perception of cellular networks, which makes full use of the existing 5G base station infrastructure, realizes the integration of communication and perception functions, and provides strong technical support for low-altitude economic construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 A distributed multi-station detection architecture for high-precision and seamless perception of cellular networks.

[0077] Figure 2 This is the target track diagram under base station tracking in the solution of the present invention.

[0078] Figure 3 It is the target reference track diagram obtained based on the error bound and the two base station measurements in the solution of the present invention.

[0079] Figure 4 This is the fused track diagram in the solution of the present invention.

[0080] Figure 5 This is a comparison chart of the errors between the solution of the present invention and the traditional fusion algorithm. DETAILED DESCRIPTION

[0081] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, and the above and / or other advantages of the present invention will become more clear.

[0082] The embodiment of the present invention provides a distributed multi-station track drawing method for high-precision seamless perception of cellular networks, comprising the following steps:

[0083] Step 1: Obtain the total number of base stations, base station measurement data and the initial state of the target, establish a target motion model, calculate the measurement error bound based on the signal-to-noise ratio of the base station, and perform extended Kalman filtering on the measurement data of each base station to obtain each local track;

[0084] Step 2: In a given overlapping coverage area, the tracks of the overlapping coverage areas of each base station are aligned in time and space and then associated. According to the track measurement values ​​and error bounds of the overlapping coverage areas of each base station that have been successfully associated, the reference measurement is obtained through maximum likelihood estimation. The track is started using the auxiliary reference measurement of the private coverage area track of the original base station, and the reference track is obtained through extended Kalman filtering.

[0085] Step 3, calculate the reference error between the reference track and the tracks of the overlapping coverage areas of each base station successfully associated in step 2, dynamically assign weights according to the error, fuse the tracks of the overlapping coverage areas of each base station, and obtain a high-precision track of the overlapping coverage area; use the fused track of the overlapping coverage area to assist the next base station in re-filtering, and update the track of the private coverage area of ​​the base station; connect the updated track of each base station's private coverage area with the fused track of the overlapping coverage area to obtain the overall situation of the target track.

[0086] Step 1 includes:

[0087] Step 1-1, establish the following linear system:

[0088] (1),

[0089] (2),

[0090] in express The state vector at time, express The moment measurement vector, express The state transfer matrix at time , express The measurement matrix of time, express The process noise distribution matrix at time , represents the process noise, represents the measurement noise, and All are zero-mean Gaussian white noise;

[0091] Step 1-2, establish the target motion model:

[0092] For the uniform linear motion model, the state vector describing the dynamic characteristics of the system ,symbol Indicates transposition; the state transition is as follows:

[0093] (3),

[0094] in express The goal at all times The position of the axis, express The goal at all times The position of the axis, express The goal at all times The speed of the axis, express The goal at all times The speed of the axis, Indicates the target Random variations in axis speed, Indicates the target Random variations in axis speed, represents the sampling interval;

[0095] Step 1-3, calculate the measurement error bound based on the signal-to-noise ratio of each base station:

[0096] Distance measurement error standard deviation for:

[0097] (4),

[0098] in represents the speed of light constant, represents the receiver bandwidth, Indicates the base station signal-to-noise ratio;

[0099] Azimuth measurement error standard deviation for:

[0100] (5),

[0101] in represents the antenna beamwidth for measuring azimuth, Indicates the single pulse slope;

[0102] Step 1-4, perform extended Kalman filtering on the measurements obtained by the base station.

[0103] Steps 1-4 include: the dynamic equation and measurement equation of the system satisfy the Gaussian condition, expressed as:

[0104] (6),

[0105] (7),

[0106] Among them, the nonlinear function and They represent the dynamic model function and the measurement model function, respectively, and the process noise and measurement noise All obey Gaussian distribution, and the covariance of process noise is , the covariance of the measurement noise is ;

[0107] The one-step prediction of the state is expressed as:

[0108] (8),

[0109] in for The state prediction at the moment, for Time to Status prediction at each moment;

[0110] The one-step forecast of the covariance is expressed as:

[0111] (9),

[0112] in for The state estimation covariance at time , for Time to State estimation covariance prediction at time instant; For vector The Jacobian matrix of for The transposed matrix of

[0113] Measuring predicted values , the covariance of the measured predicted value is expressed as:

[0114] (10),

[0115] in for time The Jacobian matrix of for Time to The measured predicted value at time, for The transposed matrix of for The information covariance matrix at each moment;

[0116] The gain is:

[0117] (11),

[0118] in for The filter gain at time t, for The inverse matrix of

[0119] The state update equation is:

[0120] (12),

[0121] in for Status updates at all times, for The measured value at the moment;

[0122] The covariance update equation is:

[0123] (13),

[0124] in is the identity matrix of the same dimension as the covariance, for The transposed matrix of

[0125] Repeat steps 1-4 until the status update is completed and the local track of each base station is obtained. ,in is the total number of base stations, It is a base station The number of tracks.

[0126] Step 2 includes:

[0127] Step 2-1: The local tracks of each base station obtained by filtering in step 1 are Transfer to a unified rectangular coordinate system through spatial alignment, and then perform time alignment by interpolation and extrapolation of overlapping tracks of each base station;

[0128] Step 2-2, weighted track association is performed on the tracks of the overlapping coverage areas of each base station after the time-space alignment in step 2-1:

[0129] set up , for two base stations, namely the first base station and the second base station, the following formula is used to test the statistic:

[0130] (14),

[0131] in express The first base station The estimated state of the target, express The second base station The estimated state of the target, express The first base station The error covariance of the target, express The second base station The error covariance of the targets; if Below using chi-square distribution The first base station is determined based on a certain threshold obtained. The target and the second base station The targets are the same target, namely the first base station Track and the second base station Track association;

[0132] Step 2-3, for the track measurement information of the overlapping coverage area of ​​each base station that is successfully associated, the reference measurement is calculated by maximum likelihood estimation according to the error bound:

[0133] (15),

[0134] in represents the likelihood function, represents the probability density function of the first track measurement value in the successfully associated track pair, represents the probability density function of the second track measurement value in the successfully associated track pair, where the probability density function of each measurement value is It is expressed as:

[0135] (16),

[0136] in Indicates the track The measured value is in the rectangular coordinate system The position on the axis, Indicates the track The measured value is in the rectangular coordinate system The position on the axis, Indicates the track The distance measurement error of the measured value in the polar coordinate system, Indicates the track The measurement error of the azimuth of the measured value in the polar coordinate system, Indicates the track The distance of the measured value in the polar coordinate system, Indicates the track The azimuth of the measured value in the polar coordinate system, Indicates the track The reference measurement is in rectangular coordinates. The position on the axis, Indicates the track The reference measurement is in rectangular coordinates. The position on the axis, Indicates the track The distance measured in polar coordinates is Indicates the track The azimuth of the reference measurement in the polar coordinate system; exp represents the natural exponential function;

[0137] Step 2-4: Use the method of step 1-4 to perform extended Kalman filtering on the reference measurement obtained in step 2-3 to obtain the target Reference track filter value (The target i here refers to the target i confirmed after the successful association of track i and track j, that is, two successfully associated tracks determine one target, and the reference measurement and reference track are also for this determined target).

[0138] Step 3 includes:

[0139] Step 3-1, calculate the reference error between the reference track and the track of the overlapping coverage area of ​​each base station successfully associated in step 2-2, and for the target determined by the successful track association , the formula is:

[0140] (17),

[0141] in express Time base station The detection The filtered value of the target, express Moment The filtered value of the target reference track, express Time base station No. The reference error between the target overlap area track and the reference track;

[0142] Step 3-2, target The reference error is normalized:

[0143] (18),

[0144] in express Time to When the state of each target is fused, the base station The weight of participation;

[0145] Step 3-3, dynamically weighted fusion of the tracks of the overlapping coverage areas of the base stations successfully associated in step 2-2, The inverse of the covariance matrix and the weighted accumulation of the state vector:

[0146] (19),

[0147] (20),

[0148] in, express The target detected by each base station at any time The weighted accumulation of the inverse of the track covariance matrix of the overlapping coverage area, express Time base station Detection target The inverse of the covariance matrix of express The target detected by each base station at any time The weighted accumulation of the track state vectors in the overlapping coverage area, express Time base station Detection target The overlapping coverage area track state vector;

[0149] Step 3-4, calculate the fused error covariance matrix and state estimation;

[0150] Step 3-5, if the target flies from the overlapping coverage area into the private coverage area of ​​a base station, the error covariance matrix and state estimation fused in step 3-4 are used to assist the base station in re-filtering and update the private coverage area track of the base station; the updated private coverage area track of each base station and the fused overlapping coverage area track are connected to obtain the overall situation of the target track.

[0151] In step 3-4, the following formula is used to calculate the fused error covariance matrix and state estimate:

[0152] (twenty one),

[0153] (twenty two),

[0154] in, express Time target The error covariance matrix after fusion, express Time target State estimation after fusion.

[0155] The present invention also provides an electronic device, comprising a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor executes the steps of the described method.

[0156] The present invention also provides a storage medium storing a computer program or instruction. When the computer program or instruction is run on a computer, the steps of the method described are executed.

[0157] In this embodiment, the fusion error between the method of the present invention and the traditional fast CI fusion algorithm is analyzed, and the simulation parameters are set as shown in Table 1 below.

[0158] Table 1

[0159]

[0160] Follow the steps below to implement it:

[0161] Step 1: Design a simulation scenario of a distributed multi-station detection architecture for high-precision seamless perception of cellular networks, such as Figure 1 As shown in the figure, the error bound is calculated based on the signal-to-noise ratio of each base station, and the extended Kalman filter is performed on the base station measurement to obtain the local track of the target tracked by each base station as shown in the figure. Figure 2 shown.

[0162] Step 2: In a given overlapping coverage area, the tracks of the overlapping coverage areas of each base station are aligned in time and space and then associated. The reference measurement is obtained by maximum likelihood estimation based on the measurement value and error bound of the successfully associated overlapping coverage area track, and the extended Kalman filter is used to assist the original base station's private coverage area track to obtain the reference track. Figure 3 shown.

[0163] Step 3, calculate the error between the reference track and the tracks of the overlapping coverage areas of each base station successfully associated in (2), dynamically assign weights based on the error, fuse the tracks of the overlapping coverage areas of each base station, and obtain a high-precision track of the overlapping coverage area. Use the fused overlapping coverage area track to assist the next base station in re-filtering and update the private coverage area track of the base station. Connect the updated private coverage area tracks of each base station and the fused overlapping coverage area tracks to obtain the overall situation of the target track. The fused complete target track is shown in Figure 4 shown.

[0164] Figure 5 The following are the tracking errors of different tracks in the simulation scenario of cross-base station target tracking. The black curve is the filtered track error of the first base station, the green curve is the filtered track error of the second base station, the blue curve is the fused track error of the classic fast CI algorithm, and the red curve is the track error of the distributed multi-station track drawing method for high-precision seamless perception of cellular networks proposed in the present invention. In the overlapping coverage area, the average filtered track error of the first base station is 1.48m, the average filtered track error of the second base station is 0.99m, the average fused track error of the classic fast CI algorithm is 0.51m, and the average track error of the distributed multi-station track drawing method for high-precision seamless perception of cellular networks proposed in the present invention is 0.35m.

[0165] In summary, the method of the present invention solves the problem of incomplete track of cross-base station target tracking. The verification analysis results show that compared with the traditional fast CI fusion algorithm, the method of the present invention achieves complete track tracking of cross-base station targets and improves the track accuracy of target tracking.

[0166] The present invention provides a distributed multi-station track drawing method for high-precision seamless perception of cellular networks. There are many methods and ways to implement the technical solution. The above is only a preferred implementation of the present invention. It should be pointed out that for ordinary technicians in this technical field, without departing from the principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention. All components not specified in this embodiment can be implemented by existing technologies.

Claims

1. A distributed multi-station track drawing method for high-precision seamless perception of cellular networks, characterized in that: The following steps are involved: Step 1: Obtain the total number of base stations, base station measurement data and the initial state of the target, establish a target motion model, calculate the measurement error bound based on the signal-to-noise ratio of the base station, and perform extended Kalman filtering on the measurement data of each base station to obtain each local track; Step 2: In a given overlapping coverage area, the tracks of the overlapping coverage areas of each base station are aligned in time and space and then associated. According to the track measurement values ​​and error bounds of the overlapping coverage areas of each base station that have been successfully associated, the reference measurement is obtained through maximum likelihood estimation. The track is started using the auxiliary reference measurement of the private coverage area track of the original base station, and the reference track is obtained through extended Kalman filtering. Step 3, calculating the reference error between the reference track and the tracks of the overlapping coverage areas of each base station successfully associated in step 2, dynamically allocating weights according to the error, performing track fusion on the tracks of the overlapping coverage areas of each base station, and obtaining a high-precision track of the overlapping coverage areas; The fused overlapping coverage area tracks are used to assist the next base station in re-filtering and update the base station's private coverage area tracks; The updated private coverage area track of each base station is connected with the fused overlapping coverage area track to obtain the overall situation of the target track.

2. The method according to claim 1, characterized in that: Step 1 includes: Step 1-1, establish the following linear system: （1）， （2）， in express The state vector at time, express The moment measurement vector, express The state transfer matrix at time , express The measurement matrix of time, express The process noise distribution matrix at time , represents the process noise, represents the measurement noise, and All are zero-mean Gaussian white noise; Step 1-2, establish the target motion model: For the uniform linear motion model, the state vector describing the dynamic characteristics of the system ,symbol Indicates transposition; the state transition is as follows: （3）， in express The goal at all times The position of the axis, express The goal at all times The position of the axis, express The goal at all times The speed of the axis, express The goal at all times The speed of the axis, Indicates the target Random variations in axis speed, Indicates the target Random variations in axis speed, represents the sampling interval; Step 1-3, calculate the measurement error bound based on the signal-to-noise ratio of each base station: Distance measurement error standard deviation for: （4）， in represents the speed of light constant, represents the receiver bandwidth, Indicates the base station signal-to-noise ratio; Azimuth measurement error standard deviation for: （5）， in represents the antenna beamwidth for measuring azimuth, Indicates the single pulse slope; Step 1-4, perform extended Kalman filtering on the measurements obtained by the base station.

3. The method according to claim 2, characterized in that Steps 1-4 include: the dynamic equation and measurement equation of the system satisfy the Gaussian condition, expressed as: （6）， （7）， Among them, the nonlinear function and They represent the dynamic model function and the measurement model function, respectively, and the process noise and measurement noise All obey Gaussian distribution, and the covariance of process noise is , the covariance of the measurement noise is .

4. The method according to claim 3, characterized in that Steps 1-4 also include: The one-step prediction of the state is expressed as: （8）， in for The state prediction at the moment, for Time to State prediction at a given moment.

5. The method according to claim 4, characterized in that Steps 1-4 also include: The one-step forecast of the covariance is expressed as: （9）， in for The state estimation covariance at time , for Time to State estimation covariance prediction at time instant; For vector The Jacobian matrix of for The transposed matrix of Measuring predicted values , the covariance of the measured predicted value is expressed as: （10）， in for time The Jacobian matrix of for Time to The measured predicted value at time, for The transposed matrix of for The information covariance matrix at each moment; The gain is: （11）， in for The filter gain at time t, for The inverse matrix of The state update equation is: （12）， in for Status updates at all times, for The measured value at the moment; The covariance update equation is: （13）， in is the identity matrix of the same dimension as the covariance, for The transposed matrix of Repeat steps 1-4 until the status update is completed and the local track of each base station is obtained. ,in is the total number of base stations, It is a base station The number of tracks.

6. The method according to claim 5, characterized in that Step 2 includes: Step 2-1: The local tracks of each base station obtained by filtering in step 1 are The spatial alignment is transferred to a unified rectangular coordinate system, and then the overlapping tracks of each base station are interpolated and extrapolated for time alignment; Step 2-2, weighted track association is performed on the tracks of the overlapping coverage areas of each base station after the time-space alignment in step 2-1: set up , for two base stations, namely the first base station and the second base station, the following formula is used to test the statistic: （14）， in express The first base station The estimated state of the target, express The second base station The estimated state of the target, express The first base station The error covariance of the target, express The second base station The error covariance of the targets; if Below using chi-square distribution The first base station is determined based on a certain threshold obtained. The target and the second base station The targets are the same target, namely the first base station Track and the second base station Track association; Step 2-3, for the track measurement information of the overlapping coverage area of ​​each base station that is successfully associated, the reference measurement is calculated by maximum likelihood estimation according to the error bound: （15）， in represents the likelihood function, represents the probability density function of the first track measurement value in the successfully associated track pair, represents the probability density function of the second track measurement value in the successfully associated track pair, where the probability density function of each measurement value is It is expressed as: （16）， in Indicates the track The measured value is in the rectangular coordinate system The position on the axis, Indicates the track The measured value is in the rectangular coordinate system The position on the axis, Indicates the track The distance measurement error of the measured value in the polar coordinate system, Indicates the track The measurement error of the azimuth of the measured value in the polar coordinate system, Indicates the track The distance of the measured value in the polar coordinate system, Indicates the track The azimuth of the measured value in the polar coordinate system, Indicates the track The reference measurement is in rectangular coordinates. The position on the axis, Indicates the track The reference measurement is in rectangular coordinates. The position on the axis, Indicates the track The distance measured in polar coordinates, Indicates the track The azimuth of the reference measurement in the polar coordinate system; exp represents the natural exponential function; Step 2-4: Use the method of step 1-4 to perform extended Kalman filtering on the reference measurement obtained in step 2-3 to obtain the target Reference track filter value .

7. The method according to claim 6, characterized in that Step 3 includes: Step 3-1, calculate the reference error between the reference track and the track of the overlapping coverage area of ​​each base station successfully associated in step 2-2, and for the target determined by the successful track association , the formula is: （17）， in express Time base station The detection The filtered value of the target, express Moment The filtered value of the target reference track, express Time base station No. The reference error between the target overlap area track and the reference track; Step 3-2, target The reference error is normalized: （18）， in express Time to When the state of each target is fused, the base station The weight of participation; Step 3-3, dynamically weighted fusion of the tracks of the overlapping coverage areas of the base stations successfully associated in step 2-2, The inverse of the covariance matrix and the weighted accumulation of the state vector: （19）， （20）， in, express The target detected by each base station at any time The weighted accumulation of the inverse of the track covariance matrix of the overlapping coverage area, express Time base station Detection target The inverse of the covariance matrix of express The target detected by each base station at any time The weighted accumulation of the track state vectors in the overlapping coverage area, express Time base station Detection target The overlapping coverage area track state vector; Step 3-4, calculate the fused error covariance matrix and state estimation; Step 3-5, if the target flies from the overlapping coverage area into the private coverage area of ​​a base station, the error covariance matrix and state estimation fused in step 3-4 are used to assist the base station in re-filtering and update the private coverage area track of the base station; the updated private coverage area track of each base station and the fused overlapping coverage area track are connected to obtain the overall situation of the target track.

8. The method according to claim 7, characterized in that In step 3-4, the following formula is used to calculate the fused error covariance matrix and state estimate: （21）， （22）， in, express Time target The error covariance matrix after fusion, express Time target The fused state estimate.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores program codes, and when the program codes are executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 8.

10. A storage medium, characterized in that: A computer program or instruction is stored, and when the computer program or instruction is run on a computer, the steps of the method according to any one of claims 1 to 8 are executed.

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