A multi-target fusion and tracking method based on coherent dual-base MIMO radar

Abstract

The application discloses a kind of multi-target fusion, tracking method based on phase comparison double-base MIMO radar. Through ESPRIT algorithm, transmitting azimuth DoD and receiving azimuth DoA estimation are realized, and the joint estimation result of double-base radar to target position is obtained. Through intermediate distance variable matching, the calculation result of target position of receiving radar is obtained. The target position calculation result of transmitting and receiving radar, receiving radar and the joint estimation result of double-base radar are matched, and then the vector velocity of target is obtained. According to multi-dimensional Kalman filtering of target multidimensional position result and vector velocity, target tracking is realized. The application has the advantages of significantly improving fusion accuracy, target tracking accuracy, and shortening the time length from target detection to target tracking.

Description

Technical Field

[0001] This invention belongs to the field of millimeter-wave radar detector technology, and particularly relates to the field of radar target detection fusion technology, especially a multi-target fusion and tracking method based on coherent bistatic MIMO radar. Background Technology

[0002] With the development of vehicle-road cooperation and autonomous driving industries, multi-sensor fusion has become a key research direction for improving system perception capabilities, achieving precise perception and holographic perception, and empowering autonomous driving. Multi-radar fusion can expand the detection range and improve detection accuracy. Among them, the field of millimeter-wave radar fusion for multiple MIMO standards has become a hot topic. Existing multi-radar fusion in the transportation field is mainly based on non-coherent mode to build the system. Each radar sensor works independently and calculates independently. Target fusion is performed at edge nodes or in the cloud. The fusion method is based on the target trajectory, which has disadvantages such as low fusion accuracy and long time from target detection to target tracking. Summary of the Invention

[0003] The purpose of this invention is to address the problems existing in the prior art by providing a multi-target fusion and tracking method based on coherent bistatic MIMO radar. Utilizing the principle of target diffuse reflection, and based on bistatic coherent MIMO radar, the invention enables the bistatic radar to output at least three detection results for the same target. After matching and fusing the three detection results, the target vector velocity information is obtained, thereby achieving four-dimensional Kalman filtering and tracking based on three position coordinates plus vector velocity, thus improving target tracking accuracy.

[0004] The technical solution to achieve the purpose of this invention is: a multi-target fusion and tracking method based on coherent bistatic MIMO radar, the method comprising the following steps:

[0005] Step 1: Construct a bistatic coherent MIMO radar system, including radar A and radar B;

[0006] Step 2: Obtain the angle between the multiple targets and the normal direction of radar A, denoted as vector θ, and obtain the angle between the multiple targets and the normal direction of radar B, denoted as vector α. Use the ESPRIT algorithm to obtain the correspondence between θ and α, and establish the matching matrix (θ, α).

[0007] Step 3: Calculate the polar coordinate matrix (R, θ) of the multiple targets in the radar A coordinate system from the matrix (θ, α), where R is the polar radius and θ is the polar angle;

[0008] Step 4: Radar A receives the transmitted signals reflected from multiple targets and calculates the polar coordinate matrix (R) of the multiple targets in the radar A coordinate system. A ,θ A ) and radial velocity matrix V A RA Let θ be the polar radius of the target in the radar A coordinate system. A The polar angle of the target in the radar A coordinate system;

[0009] Step 5: Radar B receives the transmitted signal from Radar A reflected by the target, and calculates the polar coordinate matrix (R) of the multiple targets in the radar B coordinate system. A +R B α B ) and radial velocity matrix V B ;R B Let α be the polar radius of the target in the radar B coordinate system. B The polar angle of the target in the radar B coordinate system;

[0010] Step 6, (R) A +R B α B ) and V B The result converted to radar A coordinate system ((R) A +R B )′,α B ′) and V B ′;

[0011] Step 7, for matrix (R) A ,θ A ), ((R) A +R B )′,α B Matching (R, θ) with (R, θ) yields a multi-objective coordinate matrix [(R)] that establishes the matching correspondence. A ,θ A ), (R B α B [′), (R, θ)] and the multi-target velocity matrix [V A V B ′];

[0012] Step 8, by [V] A V B ′] Obtain the target vector velocity matrix V c =(V A +V B ′);

[0013] Step 9, according to [(R A ,θ A ), (R B α B ′), (R,θ)] and V C Multi-dimensional Kalman filtering and tracking of multiple targets are performed.

[0014] Furthermore, radar A has both transmitting and receiving functions, while radar B has receiving function and may also have transmitting function.

[0015] Furthermore, the normal directions of radar A and radar B are parallel or intersecting, and radar B cannot be within the coverage area of ​​radar A's transmitted signal.

[0016] Furthermore, before step 2 is executed, the method also includes: synchronizing the bistatic radar crystal oscillator and the local oscillator signal to achieve coherence between the bistatic radar, i.e., radar A and radar B.

[0017] Furthermore, the crystal oscillator and local oscillator signals are transmitted through a radio frequency optical modulator, a radio frequency optical demodulator, or an optical fiber to achieve synchronization of the dual-base radar crystal oscillator and local oscillator signals.

[0018] Furthermore, step 3 specifically involves:

[0019] For a target M, the angle between it and the normal of radar A is θ, and the angle between it and the normal of radar B is α, where θ∈{θ}, α∈{α};

[0020] The line connecting radar A to target M is L1, and the line connecting radar B to target M is L2. The intersection of L1 and L2 is the target's location.

[0021] Based on θ, establish the equation of line L1 in the radar A coordinate system: y = a1x + b1

[0022] Based on α, through coordinate system transformation, the equation of line L2 in radar A coordinate system is established as y = a2x + b2;

[0023] Given that a1, b1, a2, and b2 are all known, solve the system of equations formed by the two equations above to obtain the position (x, y) of the target M, and convert it to polar coordinates (R, θ), where R∈{R} and θ∈{θ}.

[0024] Furthermore, the calculation method for step 5 is as follows:

[0025] Let c / (L1+L2) be the time from when radar A transmits a signal to when radar B receives the echo signal from target M, where c is the speed of light;

[0026] Based on the principle of LFMCW radar, the difference frequency f received by radar B is known. b The distance L1+L2 in the radar B coordinate system can be calculated, where (L1+L2)∈(R A +R B ).

[0027] Furthermore, step 7 involves matching using the Hungarian matching method, specifically including: first matching (R... A ,θ A ) and ((R)A +R B )′,α B Then match the result with (R, θ);

[0028] Among them (R) A ,θ A ) and ((R) A +R B )′,α B The matching process for ′) is as follows:

[0029] Assume element (R) A θ A ) and ((R) A +R B )′,α B ′) are the same objective, where (R) A θ A )∈(R A θ A ), ((R) A +R B )′,α B ′)∈((R A +R B )′,α B If ′), then the distance from the target to radar B is R. B =(R A +R B )′-R A That is, the target position expression is transformed into (R A θ A ) and (R B α B After matching, the matrix ((R) is complete. A +R B )′,α B ′) transformed into (R B α B ′);

[0030] After completing the polar coordinate transformation and matching, the velocity matching matrix [V] can be established simultaneously. A V B ′).

[0031] Compared with the prior art, the significant advantages of this invention are:

[0032] 1) This invention enables bistatic radar to output at least three detection results for the same target through a coherent method. After the three results are matched and fused, the target vector velocity information is obtained, thereby realizing four-dimensional Kalman filtering and tracking of three position coordinates plus vector velocity, and improving the target tracking accuracy.

[0033] 2) This invention has the advantages of significantly improving fusion accuracy and shortening the time from target detection to target tracking.

[0034] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of a dual-base coherent MIMO radar system in one embodiment, with the normals of the two radars parallel to each other. A typical application scenario is an urban intersection. One radar is mounted on a traffic light pole, and the other is mounted on a traffic enforcement camera pole.

[0036] Figure 2 This is a schematic diagram of a dual-base coherent MIMO radar system in one embodiment, with the normals of the two radars perpendicular to each other. A typical application scenario is an urban intersection. The two radars are mounted on traffic light poles facing different directions.

[0037] Figure 3 This is a flowchart of a multi-target fusion and tracking method based on coherent bistatic MIMO radar. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0039] In one embodiment, combined Figure 3 This paper presents a multi-target fusion and tracking method based on coherent bistatic MIMO radar, including the following steps:

[0040] Step 1: Construct a bistatic coherent MIMO radar system, including radar A and radar B. Then, synchronize the bistatic radar crystal oscillator and local oscillator signals to achieve coherence between the bistatic radar, i.e., radar A and radar B.

[0041] Here, radar A has both transmitting and receiving functions, and radar B has receiving function and may also have transmitting function; the normal directions of radar A and radar B are parallel or intersecting, and radar B cannot be within the coverage area of ​​radar A's transmitted signal.

[0042] like Figure 1 The diagram shows a schematic of a dual-base coherent MIMO radar system, where the normals of the two radars are parallel. A typical application scenario is an urban intersection, where one radar is mounted on a traffic light pole and the other on a traffic enforcement camera pole.

[0043] like Figure 2The diagram shows a schematic of a dual-base coherent MIMO radar system, where the normals of the two radars are perpendicular. A typical application scenario is an urban intersection, where the two radars are mounted on traffic light poles facing different directions.

[0044] If radar A operates in transmit + receive mode and radar B operates only in receive mode, the bistatic radar can output 3 detection results for the same target; if both radar A and radar B operate in transmit and receive modes, the bistatic radar can output up to 6 detection results for the same target.

[0045] Here, the crystal oscillator and local oscillator signals are transmitted through a radio frequency optical modulator, a radio frequency optical demodulator, or an optical fiber to achieve synchronization of the dual-base radar crystal oscillator and local oscillator signals.

[0046] Step 2: Obtain the angle between the multiple targets and the normal direction of radar A, denoted as vector θ, and obtain the angle between the multiple targets and the normal direction of radar B, denoted as vector α. Use the ESPRIT algorithm (Estimation of Signal Parametrs Via Rotational Invariance Techniques) to obtain the correspondence between θ and α, and establish the matching matrix (θ, α).

[0047] Step 3: Calculate the polar coordinate matrix (R, θ) of the multiple targets in the radar A coordinate system from the matrix (θ, α), where R is the polar radius and θ is the polar angle; the specific process includes:

[0048] Combination Figure 1 For a target M, the angle between it and the normal of radar A is θ, and the angle between it and the normal of radar B is α, where θ∈{θ}, α∈{α};

[0049] The line connecting radar A to target M is L1, and the line connecting radar B to target M is L2. The intersection of L1 and L2 is the target's location.

[0050] Based on θ, establish the equation of line L1 in the radar A coordinate system: y = a1x + b1

[0051] Based on α, through coordinate system transformation, the equation of line L2 in radar A coordinate system is established as y = a2x + b2;

[0052] Given that a1, b1, a2, and b2 are all known, solve the system of equations formed by the two equations above to obtain the position (x, y) of the target M, and convert it to polar coordinates (R, θ), where R∈{R} and θ∈{θ}.

[0053] Step 4: Radar A receives the transmitted signals reflected from multiple targets and calculates the polar coordinate matrix (R) of the multiple targets in the radar A coordinate system. A ,θ A) and radial velocity matrix V A R A Let θ be the polar radius of the target in the radar A coordinate system. A The polar angle of the target in the radar A coordinate system;

[0054] Step 5: Radar B receives the transmitted signal from Radar A reflected by the target, and calculates the polar coordinate matrix (R) of the multiple targets in the radar B coordinate system. A +R B ,αα B ) and radial velocity matrix V B ;R B Let α be the polar radius of the target in the radar B coordinate system. B The polar angle of the target in the radar B coordinate system; the calculation method is as follows:

[0055] Combination Figure 1 Let c / (L1+L2) be the time from when radar A transmits a signal to when radar B receives the echo signal from target M, where c is the speed of light.

[0056] Based on the principle of LFMCW radar, the difference frequency f received by radar B is known. b The distance L1+L2 in the radar B coordinate system can be calculated, where (L1+L2)∈(R A +R B ).

[0057] Here, the time delay caused by the transmission of crystal oscillator and local oscillator signals through optical fiber should be eliminated during the calculation process.

[0058] Step 6, (R) A +R B α B ) and V B The result converted to radar A coordinate system ((R) A +R B )′,α B ′) and V B ′;

[0059] Step 7, for matrix (R) A ,θ A ), ((R) A +R B )′,α B Matching (R, θ) with (R, θ) yields a multi-objective coordinate matrix [(R)] that establishes the matching correspondence. A ,θ A ), (R B α B [′), (R, θ)] and the multi-target velocity matrix [V A V B ′];

[0060] Preferably, the matching is performed using the Hungarian matching method, the specific process of which includes: first matching (R... A ,θ A ) and ((R) A +R B )′,α B Then match the result with (R, θ);

[0061] Among them (R) A ,θ A ) and ((R) A +R B )′,α B The matching process for ′) is as follows:

[0062] Assume element (R) A θ A ) and ((R) A +R B )′,α B ′) are the same objective, where (R) A θ A )∈(R A θ A ), ((R) A +R B )′,α B ′)∈((R A +R B )′,α B If ′), then the distance from the target to radar B is R. B =(R A +R B )′-R A That is, the target position expression is transformed into (R A θ A ) and (R B α B After matching, the matrix ((R) is complete. A +R B )′,α B ′) transformed into (R B ,α B ′);

[0063] After completing the polar coordinate transformation and matching, the velocity matching matrix [V] can be established simultaneously. A V B ′).

[0064] Step 8, by [V] A V B ′] Obtain the target vector velocity matrix V C =(V A +V B ′);

[0065] Step 9, with [(R A ,θ A ), (R B α B ′), (R,θ)] and V C As parameters, multi-dimensional Kalman filtering and tracking of multiple targets are performed.

[0066] In one embodiment, a multi-target fusion and tracking system based on coherent bistatic MIMO radar is provided, the system comprising:

[0067] The first module is used to construct a bistatic coherent MIMO radar system, including radar A and radar B; it is also used to synchronize the bistatic radar crystal oscillator and local oscillator signal to achieve coherence between the bistatic radar, i.e., radar A and radar B.

[0068] The second module is used to obtain the angle between multiple targets relative to the normal direction of radar A, denoted as vector θ, and to obtain the angle between multiple targets relative to the normal direction of radar B, denoted as vector α. The correspondence between θ and α is obtained by the ESPRIT algorithm, and a matching matrix (θ, α) is established.

[0069] The third module is used to calculate the polar coordinate matrix (R,θ) of multiple targets in the radar A coordinate system from the matrix (θ, α), where R is the polar radius and θ is the polar angle.

[0070] The fourth module is used to enable radar A to receive transmitted signals reflected from multiple targets, and to calculate the polar coordinate matrix (R) of the multiple targets in the radar A coordinate system. A ,θ A ) and radial velocity matrix V A R A Let θ be the polar radius of the target in the radar A coordinate system. A The polar angle of the target in the radar A coordinate system;

[0071] The fifth module is used to enable radar B to receive the transmitted signal from radar A reflected by the target, and to calculate the polar coordinate matrix (R) of multiple targets in the radar B coordinate system. A +R B α B ) and radial velocity matrix V B ;R B Let α be the polar radius of the target in the radar B coordinate system. B The polar angle of the target in the radar B coordinate system;

[0072] The sixth module is used to transfer (R) A +R B α B ) and V B The result converted to radar A coordinate system ((R)A +R B )′,α B ′) and V B ′;

[0073] The seventh module is used for matrices (R) A ,θ A ), ((R) A +R B )′,α B Matching (R,θ) with (R,θ) yields a multi-objective coordinate matrix [(R)] that establishes the matching correspondence. A ,θ A ), (R B α B [′), (R, θ)] and the multi-target velocity matrix [V A V B ′];

[0074] The eighth module is used by [V] A V B ′] Obtain the target vector velocity matrix V C =(V A +V B ′);

[0075] Module 9, used according to [(R A ,θ A ), (R B α B ′), (R,θ)] and V C Multi-dimensional Kalman filtering and tracking of multiple targets are performed.

[0076] Specific limitations regarding the multi-target fusion and tracking system based on coherent bistatic MIMO radar can be found in the limitations of the multi-target fusion and tracking method based on coherent bistatic MIMO radar mentioned above, and will not be repeated here. Each module in the aforementioned multi-target fusion and tracking system based on coherent bistatic MIMO radar can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independent of the processor in a computer device, or stored in software in the memory of a computer device, so that the processor can call and execute the corresponding operations of each module.

[0077] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0078] Step 1: Construct a bistatic coherent MIMO radar system, including radar A and radar B;

[0079] Step 2: Obtain the angle between the multiple targets and the normal direction of radar A, denoted as vector θ, and obtain the angle between the multiple targets and the normal direction of radar B, denoted as vector α. Use the ESPRIT algorithm to obtain the correspondence between θ and α, and establish the matching matrix (θ, α).

[0080] Step 3: Calculate the polar coordinate matrix (R, θ) of the multiple targets in the radar A coordinate system from the matrix (θ, α), where R is the polar radius and θ is the polar angle;

[0081] Step 4: Radar A receives the transmitted signals reflected from multiple targets and calculates the polar coordinate matrix (R) of the multiple targets in the radar A coordinate system. A ,θ A ) and radial velocity matrix V A R A Let θ be the polar radius of the target in the radar A coordinate system. A The polar angle of the target in the radar A coordinate system;

[0082] Step 5: Radar B receives the transmitted signal from Radar A reflected by the target, and calculates the polar coordinate matrix (R) of the multiple targets in the radar B coordinate system. A +R B α B ) and radial velocity matrix V B ;R B Let α be the polar radius of the target in the radar B coordinate system. B The polar angle of the target in the radar B coordinate system;

[0083] Step 6, (R) A +R B α B ) and V B The result converted to radar A coordinate system ((R) A +R B )′,α B ′) and V B ′;

[0084] Step 7, for matrix (R) A ,θ A ), ((R) A +R B )′,α B Matching (R, θ) with (R, θ) yields a multi-objective coordinate matrix [(R)] that establishes the matching correspondence. A ,θ A ), (R B α B [′), (R, θ)] and the multi-target velocity matrix [V A V B ′];

[0085] Step 8, by [V]A V B ′] Obtain the target vector velocity matrix V c =(V A +V B ′);

[0086] Step 9, according to [(R A ,θ A ), (R B α B ′), (R,θ)] and V C Multi-dimensional Kalman filtering and tracking of multiple targets are performed.

[0087] For specific limitations on each step, please refer to the limitations on multi-target fusion and tracking methods based on coherent bistatic MIMO radar mentioned above, which will not be repeated here.

[0088] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0089] Step 1: Construct a bistatic coherent MIMO radar system, including radar A and radar B;

[0090] Step 2: Obtain the angle between the multiple targets and the normal direction of radar A, denoted as vector θ, and obtain the angle between the multiple targets and the normal direction of radar B, denoted as vector α. Use the ESPRIT algorithm to obtain the correspondence between θ and α, and establish the matching matrix (θ, α).

[0091] Step 3: Calculate the polar coordinate matrix (R, θ) of the multiple targets in the radar A coordinate system from the matrix (θ, α), where R is the polar radius and θ is the polar angle;

[0092] Step 4: Radar A receives the transmitted signals reflected from multiple targets and calculates the polar coordinate matrix (R) of the multiple targets in the radar A coordinate system. A ,θ A ) and radial velocity matrix V A R A Let θ be the polar radius of the target in the radar A coordinate system. A The polar angle of the target in the radar A coordinate system;

[0093] Step 5: Radar B receives the transmitted signal from Radar A reflected by the target, and calculates the polar coordinate matrix (R) of the multiple targets in the radar B coordinate system. A +R B α B ) and radial velocity matrix V B ;R B Let α be the polar radius of the target in the radar B coordinate system. B The polar angle of the target in the radar B coordinate system;

[0094] Step 6, (R) A +R B α B ) and V B The result converted to radar A coordinate system ((R) A +R B )′,α B ′) and V B ′;

[0095] Step 7, for matrix (R) A ,θ A ), ((R) A +R B )′,α B Matching (R, θ) with (R, θ) yields a multi-objective coordinate matrix [(R)] that establishes the matching correspondence. A ,θ A ), (R B α B [′), (R, θ)] and the multi-target velocity matrix [V A V B ′];

[0096] Step 8, by [V] A V B ′] Obtain the target vector velocity matrix V c =(V A +V B ′);

[0097] Step 9, according to [(R A ,θ A ), (R B ,α B ′), (R,θ)] and V C Multi-dimensional Kalman filtering and tracking of multiple targets are performed.

[0098] For specific limitations on each step, please refer to the limitations on multi-target fusion and tracking methods based on coherent bistatic MIMO radar mentioned above, which will not be repeated here.

[0099] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention without departing from its spirit and scope should be included within the protection scope of the present invention.

Claims

1. A multi-target fusion and tracking method based on coherent bistatic MIMO radar, characterized in that, The method includes the following steps: Step 1: Construct a bistatic coherent MIMO radar system, including radar A and radar B; Step 2: Obtain the angle between the multiple targets and the normal direction of radar A, denoted as vector θ, and obtain the angle between the multiple targets and the normal direction of radar B, denoted as vector α. Use the ESPRIT algorithm to obtain the correspondence between θ and α, and establish the matching matrix (θ, α). Step 3: Calculate the polar coordinate matrix (R,θ) of the multiple targets in the radar A coordinate system from the matrix (θ,α), where R is the polar radius and θ is the polar angle; Step 4: Radar A receives the transmitted signals reflected from multiple targets and calculates the polar coordinate matrix of the multiple targets in the radar A coordinate system. , ) and radial velocity matrix , Let the polar radius of the target be in the radar A coordinate system. The polar angle of the target in the radar A coordinate system; Step 5: Radar B receives the transmitted signal from Radar A reflected by the target, and calculates the polar coordinate matrix of the multiple targets in the radar B coordinate system. + , ) and radial velocity matrix ; The polar radius of the target in the radar B coordinate system. The polar angle of the target in the radar B coordinate system; Step 6, ( + , )and The result converted to radar A coordinate system ( ', ')and '; Step 7, for the matrix ( , ), ( ', Matching (R,θ) with (R,θ) yields a multi-objective coordinate matrix that establishes the matching correspondence. , ), ( , '), (R,θ)] and multi-target velocity matrix [ , ']; Step 8, by [ , Obtain the target vector velocity matrix =( + '); Step 9, according to [( , ), ( , '), (R,θ)] and Multi-dimensional Kalman filtering and tracking of multiple targets; The normal directions of radar A and radar B are parallel or intersecting, and radar B cannot be within the coverage area of ​​radar A's transmitted signal.

2. The multi-target fusion and tracking method based on coherent bistatic MIMO radar according to claim 1, characterized in that, Radar A has both transmitting and receiving functions, while radar B has receiving function and may also have transmitting function.

3. The multi-target fusion and tracking method based on coherent bistatic MIMO radar according to claim 1, characterized in that, Before step 2 is executed, the method further includes: synchronizing the bistatic radar crystal oscillator and the local oscillator signal to achieve coherence between the bistatic radar, namely radar A and radar B.

4. The multi-target fusion and tracking method based on coherent bistatic MIMO radar according to claim 3, characterized in that, The crystal oscillator and local oscillator signals are transmitted through a radio frequency optical modulator, radio frequency optical demodulator, or optical fiber to achieve synchronization of the dual-base radar crystal oscillator and local oscillator signals.

5. The multi-target fusion and tracking method based on coherent bistatic MIMO radar according to claim 1, characterized in that, Step 3 specifically involves: For a target M, the angle θ between it and the normal of radar A, and the angle α between it and the normal of radar B, where θ {θ},α {α}; The line connecting radar A to target M is L1, and the line connecting radar B to target M is L2. The intersection of L1 and L2 is the target's location. Based on θ, establish the equation of line L1 in the radar A coordinate system: y = x+ Based on α, through coordinate system transformation, the equation y= for line L2 in the radar A coordinate system is established. x+ ; in , , , Given the above two equations, solve the system of equations to obtain the position (x, y) of the target M, and convert it to polar coordinates (R, θ), where R... {R}, θ {θ}.

6. The multi-target fusion and tracking method based on coherent bistatic MIMO radar according to claim 5, characterized in that, The calculation method for step 5 is as follows: Let c / (L1+L2) be the time from when radar A transmits a signal to when radar B receives the echo signal from target M, where c is the speed of light; Based on the LFMCW radar principle, the difference frequency received by radar B is known. The distance L1+L2 in the radar B coordinate system can be calculated, where (L1+L2) ( + ).

7. The multi-target fusion and tracking method based on coherent bistatic MIMO radar according to claim 1, characterized in that, Step 7 involves matching using the Hungarian matching method. The specific process includes: first matching ( , )and( ', Then match the result with (R,θ); in( , )and( ', The matching process for ') is as follows: Assuming element ( , )and( ', ') represent the same goal, where ( , ) ( , ), ( ', ') Then the distance from the target to radar B is = '- That is, the target position expression is transformed into ( , )and( , After matching, the matrix ( ', ') transformed into ( , '); After completing the polar coordinate transformation and matching, the velocity matching matrix can be established simultaneously. , ').

8. A multi-target fusion and tracking system based on coherent bistatic MIMO radar according to any one of claims 1 to 7, characterized in that, The system includes: The first module is used to construct a bistatic coherent MIMO radar system, including radar A and radar B; The second module is used to obtain the angle between multiple targets relative to the normal direction of radar A, denoted as vector θ, and to obtain the angle between multiple targets relative to the normal direction of radar B, denoted as vector α. The correspondence between θ and α is obtained by the ESPRIT algorithm, and a matching matrix (θ, α) is established. The third module is used to calculate the polar coordinate matrix (R,θ) of multiple targets in the radar A coordinate system from the matrix (θ,α), where R is the polar radius and θ is the polar angle. The fourth module is used to enable radar A to receive transmitted signals reflected from multiple targets, and to calculate the polar coordinate matrix of the multiple targets in the radar A coordinate system. , ) and radial velocity matrix , Let the polar radius of the target be in the radar A coordinate system. The polar angle of the target in the radar A coordinate system; The fifth module is used to enable radar B to receive the transmitted signal from radar A reflected by the target, and to calculate the polar coordinate matrix of multiple targets in the radar B coordinate system. + , ) and radial velocity matrix ; The polar radius of the target in the radar B coordinate system. The polar angle of the target in the radar B coordinate system; The sixth module is used to ( + , )and The result converted to radar A coordinate system ( ', ')and '; Module 7 is used for matrices ( , ), ( ', Matching (R,θ) with (R,θ) yields a multi-objective coordinate matrix that establishes the matching correspondence. , ), ( , '), (R,θ)] and multi-target velocity matrix [ , ']; Module 8, used by [ , Obtain the target vector velocity matrix =( + '); Module 9, used according to [( , ), ( , '), (R,θ)] and Multi-dimensional Kalman filtering and tracking of multiple targets are performed.

9. The multi-target fusion and tracking system based on coherent bistatic MIMO radar according to claim 8, characterized in that, The first module is also used to synchronize the bistatic radar crystal oscillator and the local oscillator signal, so as to achieve coherence of the bistatic radar, namely radar A and radar B.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes a computer program, it implements the method as described in any one of claims 1 to 7.

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