A multi-radar multi-target track fusion data simulation method and device

Abstract

The application discloses a multi-radar multi-target track fusion data simulation method and device, relates to the field of radar detection, and comprises the following steps: determining the positioning error distribution characteristics of a Beidou positioning device according to the positioning error statistics of the Beidou positioning device; simulating the Beidou positioning point coordinates of each radar in a geographic coordinate system according to the positioning error distribution characteristics of the Beidou positioning device; simulating the flight path of each target according to the parameter data of each target; determining the detection error distribution characteristics of each radar according to the detection error statistics of each radar; and then simulating the detection track point data obtained by each radar for each target. The application fully considers the influence of error factors in the real world, approximates the real effect, can be used as a data source for debugging, testing and experiment of a track fusion system, can effectively improve the development efficiency of the track fusion system, and reduces the performance verification cost of the track fusion system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of radar detection, in particular to a multi-radar multi-target track fusion data simulation method and device. BACKGROUND

[0002] In the combat of modern air defense weapon system, track fusion technology is an important means to improve the overall combat effectiveness of the system. In order to accurately perceive and master the situation of the target in the complex and changeable battlefield environment, the multi-radar cooperative working mode is usually adopted, and complete and consistent track information is generated through multi-source information fusion. This process can effectively improve the situation awareness capability of the system and provide more accurate decision basis for combat command. Therefore, the track fusion technology plays an irreplaceable important role in air defense combat and is an important link to realize cooperative combat. However, the performance of the track fusion system directly determines the effect of battlefield perception, and a series of test and verification means are needed to ensure its reliability and practical applicability.

[0003] At present, the test of the track fusion system mainly depends on the real flight test and radar data acquisition. Although this method can truly reflect the performance of the system, it has the defects of great difficulty in organization and high test cost. It needs a lot of manpower and material resources to coordinate multiple radar systems, flight targets and command systems, and the test process is complex. In addition, the test cost is high, especially when the system needs to be adjusted frequently. The natural environment restrictions (such as weather, airspace, etc.) further affect the test frequency and efficiency, and it is difficult to carry out at any time and any place, and some typical routes cannot be fully verified. In addition, external factors such as radar positioning accuracy, target detection capability and other variables affect the reliability of the test results.

[0004] Therefore, how to provide an efficient and low-cost simulation environment for the test of the track fusion system, which can approach the real combat effect and reduce the interference of external conditions, is a problem that the technical personnel in the field urgently need to solve. The solution to this problem will greatly improve the development efficiency of the track fusion system and the accuracy of the test and verification, and will help to promote the wide application of track fusion technology in air defense systems. SUMMARY

[0005] The purpose of the present application is to provide a multi-radar multi-target track fusion data simulation method and device, which can provide a simulation environment approaching the real effect for the test of the track fusion system.

[0006] To achieve the above purpose, the present application provides the following solutions:

[0007] In a first aspect, the present application provides a multi-radar multi-target track fusion data simulation method, comprising:

[0008] According to the positioning error statistics of the Beidou positioning device, the positioning error distribution characteristics of the Beidou positioning device are determined.

[0009] According to the positioning error distribution characteristics of the Beidou positioning device, Beidou positioning point coordinates of each radar in a geographic coordinate system are simulated;

[0010] According to parameter data of each target, a flight path of each target is simulated; the parameter data includes a starting position and a speed, and the flight path is time sequence position data composed of positions of the target at different times, and a time sequence interval time is less than a radar scanning period;

[0011] According to detection error statistics of each radar, detection error distribution characteristics of each radar are determined;

[0012] According to the Beidou positioning point coordinates of each radar, the detection error distribution characteristics of each radar, and the flight path of each target, detection track point data of each target obtained by each radar is simulated.

[0013] Optionally, the positioning error distribution characteristics of the Beidou positioning device are as follows:

[0014]

[0015] wherein, X Δ is an error of positioning data of the Beidou positioning device in an x direction, Y Δ is an error of the positioning data of the Beidou positioning device in a y direction, the x direction and the y direction are directions of two coordinate axes in a rectangular coordinate system established with a preset position as a coordinate origin, μ is a mean value of the positioning error of the Beidou positioning device, σ b is a standard deviation of the positioning error of the Beidou positioning device, N(μ,σ b 2 is a normal distribution with the mean value μ and the standard deviation σ b , and r is a circular probability positioning error of the Beidou positioning device, which is determined according to positioning error statistics of the Beidou positioning device.

[0016] Optionally, the detection error distribution characteristics of the radar are as follows:

[0017]

[0018] wherein, d Δ , θ Δ , are a distance error, an azimuth angle error, and an elevation angle error of the radar, respectively, σ d , σ θ , are standard deviations of the distance error, the azimuth angle error, and the elevation angle error, respectively, is a normal distribution with 0 as a mean value and σ d as a standard deviation, is a normal distribution with 0 as a mean value and σ θThe standard deviation is a normal distribution. With a mean of 0, It is a normal distribution with standard deviation.

[0019] Optionally, based on the BeiDou positioning point coordinates of each radar, the distribution characteristics of detection errors, and the flight path of each target, the detection track point data obtained by each radar for each target is simulated, specifically including:

[0020] The initial state of each radar is generated in a uniformly distributed manner; wherein, the initial state is the initial detection azimuth angle or the initial detection time.

[0021] Based on the initial state of each radar, simulate the detection azimuth angle of each radar at different times;

[0022] Based on the BeiDou positioning point coordinates of each radar and the flight path of each target, determine the coordinates of each target in the spherical coordinate system of each radar at each time.

[0023] Based on the detection azimuth angles of each radar at different times and the spherical coordinates of each target in the spherical coordinate system of each radar at different times, the position of each target detected by each radar at different times is determined; the spherical coordinates include range, azimuth angle, and elevation angle.

[0024] Based on the detection error distribution characteristics of each radar, an error is added to the position of each target detected by each radar to obtain the detection track point data; the detection track point data includes the detection track points of each radar, wherein the detection track point of the i-th radar is composed of the positions of each target detected by the i-th radar.

[0025] Optionally, based on the detection azimuth angles of each radar at different times and the spherical coordinates of each target in the spherical coordinate system of each radar at different times, the position of each target detected by each radar at different times is determined, specifically including:

[0026] When the difference between the detection azimuth angle of the i-th radar at time t and the azimuth angle of the j-th target at time t in the spherical coordinate system of the i-th radar is less than a preset threshold, the spherical coordinates of the j-th target at time t in the spherical coordinate system of the i-th radar are determined as the position of the j-th target detected by the i-th radar at time t. There is one and only one position within half of the radar scanning cycle and one and only one position within half of the radar scanning cycle time period.

[0027] Optionally, based on the positioning error distribution characteristics of the BeiDou positioning device, the coordinates of the BeiDou positioning point of each radar in the geographic coordinate system are simulated, which also includes:

[0028] Simulate BeiDou time and calibrate the clocks of each radar and target route simulation.

[0029] Secondly, this application provides a multi-radar multi-target trajectory fusion data simulation device, which applies the above-mentioned multi-radar multi-target trajectory fusion data simulation method, and the multi-radar multi-target trajectory fusion data simulation device includes:

[0030] The BeiDou position simulation module is used to determine the positioning error distribution characteristics of the BeiDou positioning device based on the positioning error statistics of the BeiDou positioning device, and to simulate the BeiDou positioning point coordinates of each radar in the geographic coordinate system based on the positioning error distribution characteristics of the BeiDou positioning device.

[0031] The target path simulation module is used to simulate the path of each target based on the parameter data of each target; the parameter data includes the starting position and velocity, and the path is time-series position data composed of the position of the target at different times, with the time interval being less than the radar scan cycle;

[0032] The radar simulation device is used to determine the detection error distribution characteristics of each radar based on the detection error statistics of each radar; and to simulate the detection track point data obtained by each radar for each target based on the BeiDou positioning point coordinates, detection error distribution characteristics, and the flight path of each target. The detection track point data includes the detection track point of each target, wherein the detection track point of the j-th target is composed of the positions of the j-th target detected by each radar at different times.

[0033] Optionally, the multi-radar multi-target trajectory fusion data simulation device further includes:

[0034] The BeiDou time simulation module is used to simulate BeiDou time and calibrate the clocks of each radar and target route simulation.

[0035] Optionally, the multi-radar multi-target trajectory fusion data simulation device further includes:

[0036] The communication simulation module is used to simulate the communication transmission delay when each radar sends its position information and detection track point data to the track fusion system.

[0037] Optionally, the multi-radar multi-target trajectory fusion data simulation device further includes:

[0038] A track fusion system is used to correlate and fuse the data of the detected track points.

[0039] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0040] This application provides a method and apparatus for simulating multi-radar, multi-target trajectory fusion data. The method includes: determining the positioning error distribution characteristics of the BeiDou positioning device based on positioning error statistics; simulating the BeiDou positioning point coordinates of each radar in the geographic coordinate system based on the positioning error distribution characteristics of the BeiDou positioning device; simulating the flight path of each target based on parameter data of each target; wherein the parameter data includes initial position and velocity, and the flight path is time-series position data composed of the target's position at different times, with the time interval being much smaller than the radar scanning cycle; determining the detection error distribution characteristics of each radar based on detection error statistics of each radar; and simulating the detection trajectory point data obtained by each radar for each target based on the BeiDou positioning point coordinates of each radar, the detection error distribution characteristics, and the flight path of each target. This application fully considers the influence of factors such as errors in the real world, approximating the real effect, and can accurately simulate various data in the trajectory fusion process. It can serve as a data source for debugging, testing, and experimentation of the trajectory fusion system, effectively improving the development efficiency of the trajectory fusion system and reducing the performance verification cost of the trajectory fusion system. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 A flowchart illustrating a multi-radar, multi-target trajectory fusion data simulation method provided in an embodiment of this application;

[0043] Figure 2 A flowchart illustrating the transformation of target simulation data position coordinates is provided in one embodiment of this application;

[0044] Figure 3 This is a schematic diagram of the structure of a multi-radar, multi-target trajectory fusion data simulation device provided in an embodiment of this application. Detailed Implementation

[0045] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] To fully verify the fusion performance of the trajectory fusion system under various typical scenarios, testing the system in a simulated environment is the best choice. The simulated environment needs to fully consider the impact of real-world errors, communication transmission delays, and other factors, with the ultimate goal of approximating the real-world effect.

[0048] In one exemplary embodiment, such as Figure 1 As shown, a multi-radar, multi-target trajectory fusion data simulation method is provided, including the following steps 101 to 105. Wherein:

[0049] Step 101: Determine the positioning error distribution characteristics of the BeiDou positioning device based on the positioning error statistics of the BeiDou positioning device.

[0050] Step 102: Based on the positioning error distribution characteristics of the BeiDou positioning device, simulate the BeiDou positioning point coordinates of each radar in the geographic coordinate system; wherein, the BeiDou positioning device is a device installed on the corresponding radar for BeiDou positioning of the radar.

[0051] Step 103: Simulate the flight path of each target based on the parameter data of each target; the parameter data includes the starting position and speed, and the flight path is time-series position data composed of the positions of the target at different times; the time interval is less than the radar scanning period, and "less than" here means much less than.

[0052] Step 104: Determine the detection error distribution characteristics of each radar based on the detection error statistics of each radar.

[0053] Step 105: Based on the BeiDou positioning point coordinates of each radar, the detection error distribution characteristics, and the flight path of each target, simulate the detection track point data obtained by each radar for each target.

[0054] After steps 101-105 above, taking into full account the influence of factors such as errors in the real world, the system approximates the real effect and can accurately simulate various data in the trajectory fusion process. It can serve as a data source for the debugging, testing, and experimentation of the trajectory fusion system, effectively improving the development efficiency of the trajectory fusion system and reducing the performance verification cost of the trajectory fusion system.

[0055] In another exemplary embodiment, step 101 above is specifically implemented as follows:

[0056] (1) Given that the CEP (Circular Error Probable) of the BeiDou positioning device is r, establish a rectangular coordinate system with the preset location as the origin. Assume that the positioning data of the BeiDou positioning device follows a two-dimensional normal distribution around the preset location, and that the errors of the positioning data of the BeiDou positioning device are independent and identically distributed in the x and y directions, and follow the same normal distribution. Let the error of the positioning data of the BeiDou positioning device in the x direction be x. Δ The error in the y-direction is Y. Δ ,but:

[0057]

[0058] Typically, the mean μ is 0, i.e., μ = 0, and the standard deviation is σ. b .

[0059] The distance R from the positioning data of the BeiDou positioning device to the set point can be expressed as:

[0060] R = (x 2 +y 2 ) 1 / 2 .

[0061] Since x and y are independent and identically distributed, R follows a Rayleigh distribution with probability density function f. R (r) is:

[0062]

[0063] The CEP is defined as the radius of the circle containing 50% of the hit points; therefore, we need to find a radius R that makes the Cumulative Distribution Function (CDF) equal to 0.5. The cumulative distribution function F of the Rayleigh distribution... R (r) is:

[0064]

[0065] Through integration, the cumulative distribution function is finally obtained as follows:

[0066]

[0067] The following equation needs to be solved:

[0068]

[0069] get:

[0070] Since the circular probability positioning error r of the BeiDou positioning device can be determined based on the positioning error statistics of the BeiDou positioning device, and given this as a known condition, the error distribution characteristics of the positioning data of the BeiDou positioning device in the x and y directions of the rectangular coordinate system can be obtained.

[0071] In another exemplary embodiment, step 102 above is specifically implemented as follows:

[0072] Based on the characteristics of positioning error distribution, the positioning data error of the BeiDou positioning device in a rectangular coordinate system can be simulated. Through coordinate transformation, the coordinates of the BeiDou positioning point for each radar in the geographic coordinate system can be obtained.

[0073] In another exemplary embodiment, step 103 above is specifically implemented as follows:

[0074] Based on the set target parameters: starting position (x0, y0, z0), the velocities at each moment in the x, y, and z directions are set by plotting curves, represented as: v x (t), v y (t), v z (t), then the target's coordinates (x) at time t. t y t , z t ),in,

[0075] In the target route simulation module, the calculation is performed every 10ms, and the target position is sent to the radar simulation module.

[0076] In another exemplary embodiment, step 104 above is specifically implemented as follows:

[0077] Radar detection information includes range d, azimuth θ, and elevation angle. They typically follow a normal distribution and are independent of each other. That is, radar error:

[0078] Distance error d Δ :

[0079] Azimuth error θ Δ :

[0080] Pitch angle error

[0081] Where, σ d σ θ , These are the standard deviations of range error, azimuth error, and elevation error, respectively. They are typically characteristics of the radar and are known parameters. Δ θ Δ , These are the radar's range error, azimuth error, and elevation error, respectively. With a mean of 0 and a mean of σ d The standard deviation is a normal distribution. With a mean of 0 and a mean of σ θ The standard deviation is a normal distribution. With a mean of 0, It is a normal distribution with standard deviation.

[0082] In another exemplary embodiment, step 105 is specifically implemented as follows:

[0083] (1) As Figure 2 The process involves first converting the flight path of each target to a geographic coordinate system, then to a Cartesian coordinate system with each radar as the origin, and finally to a spherical coordinate system with each radar as the origin. The target coordinates are... These are the target's distance, azimuth, and elevation angles, respectively.

[0084] Radar detection capability, i.e., the maximum detection range d of the radar. max , usually a characteristic of radar, is a known parameter. When d max >d m If the radar detects the target, it can detect it; otherwise, it cannot.

[0085] (2) To avoid all radars detecting the same target at the same time, the initial state of the radar is randomly generated, specifically using a random generation method based on a uniform distribution. The initial state is the initial detection azimuth angle or the initial detection time. This method avoids the detection azimuth angles of all radars starting at the same angle of 0 by simulating different initial times or different initial detection azimuth angles of each radar. Taking the initial detection time as an example, let the radar azimuth scanning period be T. When the test system starts working, the initial detection time t0 of the radar follows a uniform distribution, denoted as t0~Uniform(0,T), and the initial azimuth angle is 0.

[0086] (3) Simulate radar azimuth scanning of aerial targets. At any given moment, the radar azimuth scan only detects that azimuth. It rotates clockwise or counterclockwise according to the scanning period T. The radar azimuth scanning range is [0°, 360°). When the radar azimuth scanning angle is relative to the target's azimuth angle θ... m When the difference is less than a preset threshold, the radar has detected the target, and the target coordinates at time t are generated as follows: The coordinates at time t Consistent, within a time interval T / 2, there is one and only one target coordinate.

[0087] By combining the aforementioned errors, the coordinates of the radar-detected target can be obtained. for:

[0088] d = d t +d Δ ;

[0089] θ=θ t +θ Δ ;

[0090]

[0091] In another exemplary embodiment, this application also includes steps for BeiDou time simulation and communication simulation.

[0092] The data types for BeiDou time simulation include: year, month, day, hour, minute, second, and millisecond. Communication simulation is used to simulate the communication transmission delay of BeiDou positioning point coordinates and detection track point data of radar.

[0093] In one exemplary embodiment, such as Figure 3 As shown, a multi-radar multi-target trajectory fusion data simulation device is provided, the multi-radar multi-target trajectory fusion data simulation device comprising:

[0094] The BeiDou position simulation module is used to determine the positioning error distribution characteristics of the BeiDou positioning device based on the positioning error statistics of the BeiDou positioning device, and to simulate the BeiDou positioning point coordinates of each radar in the geographic coordinate system based on the positioning error distribution characteristics of the BeiDou positioning device.

[0095] The target route simulation module is used to simulate the route of each target based on the parameter data of each target; the parameter data includes the starting position and velocity, and the route is time-series position data composed of the position of the target at different times, with the time interval being less than the radar scan cycle.

[0096] The radar simulation device is used to determine the detection error distribution characteristics of each radar based on the detection error statistics of each radar; and to simulate the detection track point data obtained by each radar for each target based on the BeiDou positioning point coordinates, detection error distribution characteristics, and the flight path of each target. The detection track point data includes the detection track point of each target, wherein the detection track point of the j-th target is composed of the positions of the j-th target detected by each radar at different times.

[0097] In one exemplary embodiment, such as Figure 3 As shown, this application also includes a BeiDou time simulation module and a communication simulation module.

[0098] The above modules are software programs running on various computer devices and communicate with each other via switches.

[0099] The radar simulation device includes multiple radar simulation modules.

[0100] The BeiDou time simulation module is used to simulate BeiDou time, periodically sending BeiDou time to the target route simulation module and each radar simulation module. The period is a fixed value. After receiving the BeiDou time, the target route simulation module and each radar simulation module adjust their time to match the BeiDou time. BeiDou time data types include: year, month, day, hour, minute, second, and millisecond.

[0101] The BeiDou position simulation module is used to simulate the BeiDou positioning information of various radars. In the test system, the BeiDou position simulation module sets the position of each radar in the geographic coordinate system. The position data includes: radar ID, longitude, latitude, and altitude. The positioning data generation period of the BeiDou positioning device is a fixed value. The BeiDou position simulation module can uniformly set the positions of each radar, forming a multi-radar detection deployment. The positioning data of the BeiDou positioning device is sent to each radar simulation module at a period of 100ms. Because BeiDou equipment has positioning errors, which are usually described by circular probability error (CEP), the BeiDou position simulation module needs to simulate the positioning data of each radar with its own errors.

[0102] The target flight path simulation module simulates the flight of aerial targets, including setting target flight paths and generating the target's current flight parameters in a time-series manner according to the target flight path. Target flight parameters include: target temporal position, target friend or foe attribute, speed, target ID, etc. The target temporal position is the X, Y, Z coordinates in a Cartesian coordinate system relative to the position in the geographic coordinate system of the track fusion system. The target flight path simulation module allows setting aerial target flight path parameters, including: target ID, initial position (x0, y0, z0), speed (v...). x ,v y ,v z After setting the target (enemy / friendly attributes, friendly aircraft, unknown, etc.) and time, the target's movement is simulated according to the time sequence, and the target flight parameter data is sent to each radar simulation module.

[0103] The radar simulation module simulates the radar's detection trajectory towards a target. It receives position data from the BeiDou position simulation module, forming radar position data; it also receives target flight parameter data from the target flight path simulation module, and simulates target detection according to the radar's scan cycle and characteristics, generating real-time detection trajectory data. Radar characteristics include radar detection power and detection error; the radar scan cycle is a fixed value. Each radar simulation module sends the detection trajectory data and radar position data to the communication simulation module. The detection trajectory data includes: target ID, friend or foe attribute, range, azimuth, elevation, and detection time. The range, azimuth, and elevation are in spherical coordinates with the radar as the origin. The radar position data includes: radar ID, longitude, latitude, and altitude.

[0104] The communication simulation module simulates data latency during communication. It sends received detection track point data and radar position data to the track fusion system after a certain delay. This delay is typically a fixed value and can be set according to actual conditions. Upon receiving the detection track point data and radar position data, the track fusion system performs track association and fusion for multiple radars and targets.

[0105] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0106] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for simulating multi-radar, multi-target trajectory fusion data, characterized in that, include: Based on the positioning error statistics of the BeiDou positioning device, the positioning error distribution characteristics of the BeiDou positioning device are determined; Based on the positioning error distribution characteristics of the BeiDou positioning device, the coordinates of the BeiDou positioning point of each radar in the geographic coordinate system are simulated. Based on the parameter data of each target, the flight path of each target is simulated; the parameter data includes the starting position and velocity, and the flight path is a time-series position data composed of the position of the target at different times; the time interval is less than the radar scan cycle; Based on the detection error statistics of each radar, the detection error distribution characteristics of each radar are determined. Based on the BeiDou positioning point coordinates of each radar, the distribution characteristics of detection errors, and the flight path of each target, the detection track point data obtained by each radar for each target is simulated. Based on the BeiDou positioning point coordinates of each radar, the distribution characteristics of detection errors, and the flight path of each target, the detection track point data obtained by each radar for each target is simulated, specifically including: The initial state of each radar is generated in a uniformly distributed manner; wherein, the initial state is the initial detection azimuth angle or the initial detection time. Based on the initial state of each radar, simulate the detection azimuth angle of each radar at different times; Based on the BeiDou positioning point coordinates of each radar and the flight path of each target, determine the coordinates of each target in the spherical coordinate system of each radar at each time. Based on the detection azimuth angles of each radar at different times and the spherical coordinates of each target in the spherical coordinate system of each radar at different times, the position of each target detected by each radar at different times is determined; the spherical coordinates include range, azimuth angle, and elevation angle. Based on the detection error distribution characteristics of each radar, an error is added to the position of each target detected by each radar to obtain the detection track point data; the detection track point data includes the detection track points of each radar, wherein the detection track point of the i-th radar is composed of the positions of each target detected by the i-th radar.

2. The multi-radar multi-target trajectory fusion data simulation method according to claim 1, characterized in that, The positioning error distribution characteristics of the BeiDou positioning device are as follows: Among them, X Δ The error in the positioning data of the Beidou positioning device in the x-direction, Y Δ Let be the positioning error of the BeiDou positioning device in the y-direction, where x and y are the directions of two coordinate axes in a Cartesian coordinate system established with the preset position as the origin, μ is the mean of the positioning error of the BeiDou positioning device, and σ is the mean of the positioning error of the BeiDou positioning device. b This represents the standard deviation of the positioning error of the BeiDou positioning device. With μ as the mean and σ as the mean b denoted by a normal distribution with standard deviation, r represents the circular probability positioning error of the BeiDou positioning device, which is determined based on the positioning error statistics of the BeiDou positioning device.

3. The multi-radar multi-target trajectory fusion data simulation method according to claim 1, characterized in that, The detection error distribution characteristics of radar are as follows: Where, d Δ θ Δ , These are the radar's range error, azimuth error, and elevation error, respectively, σ d σ θ , These are the standard deviations of distance error, azimuth error, and elevation error, respectively. With a mean of 0 and a mean of σ d The standard deviation is a normal distribution. With a mean of 0 and a mean of σ θ The standard deviation is a normal distribution. With a mean of 0, It is a normal distribution with standard deviation.

4. The multi-radar multi-target trajectory fusion data simulation method according to claim 1, characterized in that, Based on the detection azimuth angles of each radar at different times and the spherical coordinates of each target in the spherical coordinate system of each radar at different times, the position of each target detected by each radar at different times is determined, specifically including: When the difference between the detection azimuth angle of the i-th radar at time t and the azimuth angle of the j-th target at time t in the spherical coordinate system of the i-th radar is less than a preset threshold, the spherical coordinates of the j-th target at time t in the spherical coordinate system of the i-th radar are determined as the position of the j-th target detected by the i-th radar at time t. Within half of the radar scanning period, there is one and only one position.

5. The multi-radar multi-target trajectory fusion data simulation method according to claim 1, characterized in that, Based on the positioning error distribution characteristics of BeiDou positioning devices, the coordinates of each radar's BeiDou positioning point in the geographic coordinate system are simulated. This also includes: Simulate BeiDou time and calibrate the clocks of each radar and target route simulation.

6. A multi-radar, multi-target trajectory fusion data simulation device, characterized in that, The multi-radar multi-target trajectory fusion data simulation device applies the multi-radar multi-target trajectory fusion data simulation method according to any one of claims 1-5, and the multi-radar multi-target trajectory fusion data simulation device comprises: The BeiDou position simulation module is used to determine the positioning error distribution characteristics of the BeiDou positioning device based on the positioning error statistics of the BeiDou positioning device, and to simulate the BeiDou positioning point coordinates of each radar in the geographic coordinate system based on the positioning error distribution characteristics of the BeiDou positioning device. The target path simulation module is used to simulate the path of each target based on the parameter data of each target; the parameter data includes the starting position and velocity, and the path is time-series position data composed of the position of the target at different times, with the time interval being less than the radar scan cycle; The radar simulation device is used to determine the detection error distribution characteristics of each radar based on the detection error statistics of each radar; and to simulate the detection track point data obtained by each radar for each target based on the BeiDou positioning point coordinates, detection error distribution characteristics, and the flight path of each target. The detection track point data includes the detection track point of each target, wherein the detection track point of the j-th target is composed of the positions of the j-th target detected by each radar at different times.

7. The multi-radar multi-target trajectory fusion data simulation device according to claim 6, characterized in that, The multi-radar multi-target trajectory fusion data simulation device also includes: The BeiDou time simulation module is used to simulate BeiDou time and calibrate the clocks of each radar and target route simulation.

8. The multi-radar multi-target trajectory fusion data simulation device according to claim 6, characterized in that, The multi-radar multi-target trajectory fusion data simulation device also includes: The communication simulation module is used to simulate the communication transmission delay when each radar sends its position information and detection track point data to the track fusion system.

9. The multi-radar multi-target trajectory fusion data simulation device according to claim 6, characterized in that, The multi-radar multi-target trajectory fusion data simulation device also includes: A track fusion system is used to correlate and fuse the data of the detected track points.

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