Radar network anti-deception interference method, system and equipment based on trajectory optimization

Abstract

The present invention provides a radar networking anti-deception interference method, system and equipment based on trajectory optimization, and relates to the field of radar networking anti-distance deception false target interference. The method includes: establishing a false target misjudgment probability mathematical model of the networking system based on the anti-distance deception interference method of the moving platform radar networking system; the moving platform radar networking system anti-distance deception interference method considers the radar position error; based on the false target misjudgment probability mathematical model, taking minimizing the false target misjudgment probability as the objective function, setting the motion constraint conditions of the networking system, and constructing a trajectory optimization model of the networking system; determining the optimal trajectory of each radar in the networking system according to the trajectory optimization model, and using the moving platform radar networking system anti-deception interference method to identify real targets and false targets. The present invention can improve the false target identification effect of the system under the condition of ensuring the real target identification effect of the radar networking system.

Description

Technical Field

[0001] The present invention relates to the field of radar networking anti-distance deception false target interference, and in particular to a radar networking anti-deception interference method, system and equipment based on trajectory optimization. Background Art

[0002] Electronic jamming technology has become increasingly complex with the rapid development of digital RF memory and electronic components. Compared with active suppression jamming, active deception jamming has the advantages of strong targeting and high flexibility. Active deception jamming modulates and forwards the received radar signal to generate false target information, thereby interfering with radar target detection, positioning and tracking.

[0003] There are many mature anti-interference methods for single radars against active deception interference, such as using transmission optimized signals, kinematic information, polarization information, digital radio frequency memory DRFM quantization error, frequency diversity, frequency agility, etc. However, a single radar can only perceive the environment from a single perspective, and compared with a networked radar system, it can obtain less information and has limited anti-interference capabilities. Thanks to its spatial distribution characteristics, the networked radar system can perceive the environment from multiple perspectives and dimensions. The use of data fusion technology can effectively improve the anti-interference performance of the radar network system.

[0004] The anti-deception jamming method based on data-level fusion for networked radar refers to transmitting the point tracks or tracks of the targets in each station radar to the fusion center, and identifying the true and false targets through point track association and track association. For the interference of distance deception false targets, it can be effectively identified based on the difference between true and false targets, that is, the real targets have spatial consistency and the false targets have spatial position dispersion. In view of the difference in the spatial distribution of the tracks of true and false targets, a track deception jamming identification method based on the mean-variance joint test can also be used to effectively identify the false tracks. Most of the publicly published literature on networked radar anti-jamming based on data-level fusion uses position information, angle information, speed information or track information to identify false targets, but the methods of these literatures are only applicable to ground-based radar networking systems. For radar networking systems on moving platforms, the influence of radar position error on the anti-jamming effect of data-level fusion must be considered to ensure a higher probability of correct identification of real targets, because it is meaningful to identify false targets only when the real targets are not misjudged. Therefore, the data-level fusion anti-jamming method of ground-based radar networking systems cannot be fully applied to moving platform networking radar systems.

[0005] Whether it is a ground-based radar network or a mobile platform radar network system, the anti-interference effect is easily affected by the spatial distribution position of each station radar. At present, most studies focus on the optimization of ground-based radar network station layout, and most of them establish mathematical models for optimizing station layout by maximizing radar network airspace coverage coefficient, key area detection coefficient, airspace overlap coverage coefficient and other radar network detection performance indicators. There are few publicly published literature on the optimization of station layout methods aimed at radar network anti-interference performance. Therefore, in a deception interference environment, studying the trajectory optimization method of the mobile platform radar network system is of great significance to effectively improve the system's false target identification effect. Summary of the invention

[0006] The purpose of the present invention is to provide a radar networking anti-deception interference method, system and equipment based on trajectory optimization to solve the problem of poor false target identification effect of a moving platform radar networking system.

[0007] To achieve the above object, the present invention provides the following solution.

[0008] A radar networking anti-deception jamming method based on trajectory optimization comprises: establishing a false target misjudgment probability mathematical model of the moving platform radar networking system based on the anti-distance deception jamming method of the moving platform radar networking system; the moving platform radar networking system anti-distance deception jamming method takes radar position error into consideration; based on the false target misjudgment probability mathematical model, minimizing the false target misjudgment probability is used as an objective function, setting constraint conditions of the moving platform radar networking system, and constructing a trajectory optimization model of the moving platform radar networking system; determining the optimal trajectory of each radar in the moving platform radar networking system according to the trajectory optimization model, and using the moving platform radar networking system anti-deception jamming method to identify real targets and false targets.

[0009] Optionally, the method for the moving platform radar networking system to resist distance deception interference specifically includes: obtaining target quantity measurements of any two radars in the moving platform radar networking system, and converting the target quantity measurements into a unified rectangular coordinate system; obtaining the measurement state of the target quantity measurement in the unified rectangular coordinate system, and calculating the error covariance matrix of the corresponding target in the radar according to the target quantity measurement in the radar and the measurement error of the radar; calculating the Mahalanobis distance between the two targets according to the measurement state and the corresponding error covariance matrix; using the Mahalanobis distance as a hypothesis test statistic for false target identification; testing and identifying distance deception false targets according to the hypothesis test statistic; performing false target identification on the radars in the moving platform radar networking system in pairs, traversing all targets of all radars in the moving platform radar networking system, and the target is judged as a real target by the moving platform radar networking system if and only if all combined judgment results on the same target in all radars are judged to be true targets, otherwise the target is judged to be a false target.

[0010] Optionally, the measurement status is: ;in, is the x-axis value of the target measurement value in a unified rectangular coordinate system; is the y-axis value of the target measurement value in the unified rectangular coordinate system; is the length of the projection of the target radial distance on the xoy plane of the unified rectangular coordinate system, , where r i is the radial distance of the target measured by the i-th radar, is the target elevation angle measured by the i-th radar; is the target azimuth measured by the i-th radar; Represents the spatial position of the i-th radar in the unified rectangular coordinate system.

[0011] Optionally, the Mahalanobis distance between two targets is calculated according to the measurement state and the corresponding error covariance matrix, specifically including: constructing a diagonal matrix consisting of the measurement error and the radar position error; constructing an error covariance matrix according to the diagonal matrix consisting of the measurement error and the radar position error; determining the coordinate state difference of the two targets in the xoy plane according to the measurement state; and calculating the Mahalanobis distance between the two targets measured in the two radars according to the coordinate state difference and the error covariance matrix.

[0012] Optionally, a false target misjudgment probability mathematical model of the moving platform radar networking system is established based on the anti-distance deception interference method of the moving platform radar networking system, specifically including: converting the two target quantity measurements measured in the two radars into a unified rectangular coordinate system, and obtaining the state coordinates of the two targets in the two radars in the xoy plane of the unified rectangular coordinate system; determining the state coordinate difference of the two targets in the two radars; for the distance deception false target, calculating the Mahalanobis distance between the two targets in the two radars according to the state coordinate difference and determining the two-dimensional probability density function of the state coordinate difference; when the target state coordinates measured in the two radars correspond to a false target but are misjudged as a real target, the probability that the Mahalanobis distance is less than or equal to the chi-square distribution threshold value is defined as the false target misjudgment probability, and determining the false target misjudgment probability model according to the double integral of the two-dimensional probability density function in the region where the Mahalanobis distance is less than or equal to the threshold value; and constructing the false target misjudgment probability mathematical model of the moving platform radar networking system as a whole according to the false target misjudgment probability model of the two radars.

[0013] Optionally, the mathematical model of false target misjudgment probability of the entire mobile platform radar networking system is for: ;in, represents the false target misjudgment probability of the i-th radar and the j-th radar; N is the number of radars in the moving platform radar networking system.

[0014] Optionally, the objective function of the trajectory optimization model is: in, is the position vector of the i-th radar at the k-th moment; D is the detection area; k is any moment; K is the total time for false target identification.

[0015] Optionally, the constraints on the motion of the moving platform radar networking system in the trajectory optimization model include constraints at the first moment and constraints at subsequent moments; wherein the constraints at the first moment are described as follows: ;in, is the minimum radius of the optimization range; is the maximum radius of the optimization range; is the minimum distance between two radars; is the position vector of the i-th radar at the first moment; is the coordinate vector of the center point of the detection area; is the position vector of the jth radar at the first moment; the constraints at the following moments are described as follows: ;in, is the position vector of the i-th radar at the k-th moment; is the position vector of the jth radar at the kth moment; Indicates the distance between the radar and the center of the target detection area at the kth moment; Indicates the distance between the radar's optimal position at the last moment and the center of the target detection area; It indicates the maximum radial distance change of the radar moving toward the target area based on the optimal position at the last moment; It indicates the minimum radial distance change of the radar moving toward the target area based on the optimal position at the previous moment; represents the azimuth of the radar at the kth moment in the rectangular coordinate system with the center point of the target area as the origin; Indicates the azimuth of the radar's optimal position at the last moment; Indicates the maximum angle at which the radar platform is allowed to maneuver.

[0016] A radar networking anti-deception jamming system based on trajectory optimization comprises: a false target misjudgment probability mathematical model establishment module, which is used to establish a false target misjudgment probability mathematical model of the moving platform radar networking system based on a moving platform radar networking system anti-distance deception jamming method; the moving platform radar networking system anti-distance deception jamming method takes radar position error into consideration; a trajectory optimization model construction module, which is used to set constraints of the moving platform radar networking system based on the false target misjudgment probability mathematical model and take minimizing the false target misjudgment probability as an objective function, and construct a trajectory optimization model of the moving platform radar networking system; and an optimal trajectory determination module, which is used to determine the optimal trajectory of each radar in the moving platform radar networking system according to the trajectory optimization model, and identify real targets and false targets based on the optimal trajectory.

[0017] An electronic device includes a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the above-mentioned radar networking anti-deception interference method based on trajectory optimization.

[0018] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: considering the radar position error, the invention is applicable to the background of a homogeneous radar networking system of a moving platform, and can solve the problem that the real target identification effect of the traditional anti-interference method deteriorates as the radar position error increases while effectively identifying false targets; at the same time, a mathematical model of the false target misjudgment probability of the moving platform radar networking system is established, and a trajectory optimization model of the moving platform radar networking system is constructed with minimizing the false target misjudgment probability as the objective function, and the constructed trajectory optimization model is solved to determine the optimal trajectory of each radar in the moving platform radar networking system, and under the condition of ensuring the correct identification probability of the real target, the identification effect of the radar networking system on the distance deception false target interference is further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0020] Figure 1 This is a flow chart of the radar networking anti-deception interference method based on trajectory optimization.

[0021] Figure 2 Schematic diagram of the anti-distance deception interference of the moving platform radar networking system.

[0022] Figure 3 Schematic diagram of the constraints at the first moment.

[0023] Figure 4 Schematic diagram of the constraint conditions at the next few moments.

[0024] Figure 5 Schematic diagram of the probability statistics of real target identification.

[0025] Figure 6 Schematic diagram of the theoretical value of the probability of identifying a true target.

[0026] Figure 7 Schematic diagram of the statistical value of false target misjudgment probability.

[0027] Figure 8 Schematic diagram of the theoretical value of the false target misjudgment probability.

[0028] Fig. 9 Schematic diagram of the error between the theoretical value and the statistical value of the false target misjudgment probability.

[0029] Fig.10 This is a schematic diagram of optimizing the station layout for simulation scenario one.

[0030] Fig.11 This is a schematic diagram of the trajectory optimization results of the radar networking system in simulation scenario one.

[0031] Fig.12 This is a schematic diagram of the trajectory optimization results of the radar networking system in simulation scenario two.

[0032] Fig.13 Schematic diagram of the impact of radar position error on the probability of true / false target identification.

[0033] Fig.14 Schematic diagram of the comparison results of the real target identification probability between the present invention and the existing method.

[0034] Fig.15Schematic diagram of false target identification probability comparison results between the present invention and the existing method. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] The purpose of the present invention is to provide a radar networking anti-deception interference method, system and equipment based on trajectory optimization, which can improve the identification effect of the moving platform radar networking system on distance deception false target interference.

[0037] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0038] Embodiment 1: Figure 1 As shown, the present invention provides a radar networking anti-deception interference method based on trajectory optimization, which includes the following steps.

[0039] In practical applications, the method for resisting distance deception interference of the moving platform radar networking system specifically includes: obtaining target quantity measurements of any two radars in the moving platform radar networking system, and converting the target quantity measurements into a unified rectangular coordinate system; obtaining the measurement state of the target quantity measurement in the unified rectangular coordinate system, and determining the error covariance matrix of different target measurement states in each radar according to the target quantity measurement, radar measurement error and radar position error; calculating the Mahalanobis distance between different targets in the two radars according to the measurement state and the error covariance matrix; using the Mahalanobis distance as a hypothesis test statistic for false target identification; and performing a chi-square test according to the hypothesis test statistic to identify distance deception false targets.

[0040] As an optional implementation mode of the present invention, a schematic diagram of the radar networking system of the moving platform against distance deception interference is shown in FIG. Figure 2 As shown. Assume that there are N radars in the network system, where r is the radial distance of the target measured by any radar. is the target azimuth measured by any radar, is the target elevation angle measured by any radar, is the position of the Nth radar, and two of the radars are used as examples to illustrate the anti-deception interference method of the mobile platform radar network system. The present invention first converts the target measurement information in each station radar from the polar coordinate system to a unified rectangular coordinate system. Figure 2As shown in the figure, since the physical source of the same real target is the same, the spatial position of its measurement information in each station radar after coordinate transformation is roughly close; the distance deception interference is due to the deception distance of the false target, and the false target is distributed on the radial connection line between the real target and each station radar, so the spatial position of the false target is dispersed after coordinate transformation. Based on the characteristics of real targets having spatial consistency and false targets having dispersion, real and false targets can be distinguished. The anti-distance deception interference method of the moving platform radar networking system considers the radar position error in the process of hypothesis testing and calculating the error covariance matrix. The anti-distance deception interference method of the moving platform radar networking system is as follows.

[0041] 1) Convert the target quantities measured by the two radars into a unified rectangular coordinate system.

[0042] Assume that the positions of the two radars are , The target quantities measured are , ,make , where xi is the horizontal coordinate of the radar in the unified rectangular coordinate system, yi is the vertical coordinate of the radar in the unified rectangular coordinate system, zi is the vertical coordinate of the radar in the unified rectangular coordinate system, and ri is the radial distance of the target measured by the i-th radar. is the target azimuth measured by the i-th radar, is the target elevation angle measured by the i-th radar, Indicates the length of the target radial distance projected on the xoy plane. .

[0043] The target measurement values ​​of the two radars are converted from their respective polar coordinate systems to the xoy plane in the unified rectangular coordinate system, that is, the measurement state of the target measurement value in the unified rectangular coordinate system is ,[.] T Represents a transpose operation.

[0044] in, , represents the spatial position of the i-th radar in the unified rectangular coordinate system, , They respectively represent the x-axis and y-axis values ​​of the target quantity measured in a unified rectangular coordinate system.

[0045] 2) The state quantity in the unified rectangular coordinate system Calculate the Mahalanobis distance and obtain the hypothesis test statistic.

[0046] Target state error of each radar Approximately obeys the mean of 0 and the variance of Gaussian distribution, error covariance matrix for: .in, is the transformation matrix, is a diagonal matrix consisting of radar measurement error and radar position error.

[0047] .

[0048] .

[0049] in, is the azimuth measurement error of the i-th radar, is the position error of the i-th radar in the x-axis direction, is the position error of the i-th radar in the y-axis direction, for The error covariance matrix of , is the transformation matrix, is a diagonal matrix composed of radar distance measurement error and pitch angle measurement error.

[0050] .

[0051] .

[0052] in, is the radial distance measurement error of the i-th radar, is the elevation angle measurement error of the i-th radar.

[0053] If the target quantity measurements in the two radars correspond to the same target, then the coordinate state difference It obeys a zero-mean Gaussian distribution. Since the measurement errors of the two radars are independent of each other, The error covariance matrix for: .

[0054] In the formula, Indicates the desired operation.

[0055] Using the coordinate state difference of the target in the xoy plane in the two radars Calculate the Mahalanobis distance: .

[0056] The Mahalanobis distance is used as a hypothesis test statistic for false target identification.

[0057] 3) Identify false targets based on the Mahalanobis distance hypothesis test.

[0058] set up Indicates the target status values ​​of the two radars , Corresponding to the same real target, the Mahalanobis distance d approximately obeys distributed; Indicates that the two target state values ​​correspond to different false targets, or the same false target, or different real targets.

[0059] The hypothesis testing model can be expressed as follows: .in, The degrees of freedom are 2 and the significance level is The chi-square distribution threshold value determined by the true target misjudgment probability for .

[0060] In order to make the hypothesis test results more accurate, the accumulated Mahalanobis distances at K moments are used as the test statistic. ,Right now: .like , indicating that the targets detected by the two radars correspond to the same target, i.e., the real target; if Indicates that the targets detected by the two radars correspond to different targets. When , every two radars identify the false target. If and only if all the combined judgment results in the same target measurement sequence are , the target is judged as a real target.

[0061] Step 101: A mathematical model of false target misjudgment probability of the moving platform radar networking system is established based on the moving platform radar networking system anti-distance deception interference method; the moving platform radar networking system anti-distance deception interference method takes radar position error into consideration.

[0062] In practical applications, the step 101 specifically includes: converting the two target quantity measurements measured in the two radars into a unified rectangular coordinate system, obtaining the state coordinates of the two targets in the two radars in the xoy plane of the unified rectangular coordinate system; determining the state coordinate difference of the two targets in the two radars; for the distance deception false target, calculating the Mahalanobis distance between the two targets in the two radars according to the state coordinate difference and determining the two-dimensional probability density function of the state coordinate difference; when the target state coordinates measured in the two radars correspond to the false target but are misjudged as the real target, the probability that the Mahalanobis distance is less than or equal to the chi-square distribution threshold value is defined as the false target misjudgment probability, and determining the false target misjudgment probability model according to the double integral of the two-dimensional probability density function in the region where the Mahalanobis distance is less than or equal to the threshold value; and constructing the false target misjudgment probability mathematical model of the overall moving platform radar networking system according to the false target misjudgment probability model of the two radars.

[0063] As an optional implementation mode of the present invention, firstly, a mathematical model of false target misjudgment probability of two radar networks is analyzed, and then a mathematical model of false target misjudgment probability of the entire moving platform radar networking system is obtained.

[0064] For the distance deception false target, the quantity measurement in the two radars is converted to a unified rectangular coordinate system, and the state coordinates in the xoy plane are: ;in, To deceive the distance, is the coordinate value of the false target measured by the i-th radar in a unified rectangular coordinate system.

[0065] If the coordinate values ​​in the unified rectangular coordinate system , When corresponding to the real target ( Conditions), state difference The mean is 0 and the covariance matrix is The normal distribution of .

[0066] like , Corresponding to the false target ( conditions), The mean is , the covariance matrix is The normal distribution is:

[0067] .

[0068] in, , is the horizontal coordinate difference between the two false targets, is the ordinate difference between the two false targets, the covariance matrix is a symmetric matrix, which can be written as:

[0069] .

[0070] In the formula, , Indicates the measurement error of the radar on the x-axis and y-axis; It represents the correlation coefficient between the x-axis measurement error and the y-axis measurement error.

[0071] The two-dimensional probability density function is:

[0072] .

[0073] calculate , The Mahalanobis distance is , which can be simplified to:

[0074] .

[0075] 1) The probability of misjudgment of false targets when two radars are networked.

[0076] If the status values ​​of the two radars , Corresponding to the false target, Indicates that the false target is misjudged as a real target.

[0077] Therefore, the probability of false target misjudgment when two radars are networked is:

[0078] .

[0079] make , then the false target misjudgment probability can be expressed as The two-dimensional probability density function of The double integral of: false target misjudgment probability .

[0080] In order to facilitate calculation, , , , It can be simplified to:

[0081] .

[0082] but: ,in, , , is the lower limit of the integral, The upper limit of points.

[0083] 2) Mathematical model of false target misjudgment probability of the overall radar networking system.

[0084] Since the target is judged as a false target only when the judgment results of all radar combinations are false targets, the target is judged as a false target. Therefore, the mathematical model of false target misjudgment probability of the radar network system as a whole can be generalized from the mathematical model of false target misjudgment probability of two radars, that is: In the formula, represents the false target misjudgment probability of the i-th radar and the j-th radar; N is the number of radars in the networking system.

[0085] Step 102: Based on the false target misjudgment probability mathematical model, taking minimizing the false target misjudgment probability as the objective function, setting the constraint conditions of the moving platform radar networking system, and constructing a trajectory optimization model of the moving platform radar networking system.

[0086] In practical applications, the detection area is set to D, and the center point position vector is , the position vector of the i-th radar at the k-th moment is , the position vector of the jth radar at the kth moment is The objective function to minimize the probability of false target misjudgment of the radar networking system is:

[0087] .

[0088] In the formula, .

[0089] The trajectory optimization problem of the radar networking system can be decomposed into the radar networking station layout problem at multiple moments, but the constraints need to be set reasonably. The constraints are as follows: (1) The detection area must always be within the maximum detection range of the radar; (2) In order to ensure the multi-perspective advantage of the radar networking system and to avoid interference between radar signals, the distance between the two radars must not be too close; (3) Each radar flies towards the target area. The constraints at the first moment are different from those at the following moments, so they need to be described separately when constructing the trajectory optimization model of the moving platform radar networking system.

[0090] The constraints at the first moment are described as follows:

[0091] st .

[0092] Depend on , The optimal range for radar station deployment at the first moment of networking was determined. is the minimum radius of the optimization range, It is the maximum radius of the optimization range and ensures that the target area is within the detection range of the radar. Indicates the minimum distance between two radars. At the first moment, the radar network optimization station range is as follows Figure 3 The dark area is shown.

[0093] The constraints at the next few moments are described as follows:

[0094] st .

[0095] In the formula, Indicates the distance between the radar and the center of the target detection area at the kth moment; Indicates the distance between the radar's optimal position at the last moment and the center of the target detection area; It indicates the maximum radial distance change of the radar moving toward the target area based on the optimal position at the last moment; It indicates the minimum radial distance change of the radar moving toward the target area based on the optimal position at the previous moment; represents the azimuth of the radar at the kth moment in the rectangular coordinate system with the center point of the target area as the origin; Indicates the azimuth of the radar's optimal position at the last moment; represents the maximum angle at which the radar platform is allowed to maneuver. This constraint is described as follows Figure 4 As shown, is the optimal position of the radar at the previous moment, and the area framed by the black line represents the optimal position search area of ​​the radar at the next moment.

[0096] Step 103: Determine the optimal trajectory of each radar in the moving platform radar networking system according to the trajectory optimization model, and use the moving platform radar networking system anti-deception interference method to identify real targets and false targets.

[0097] In practical applications, the particle swarm optimization (PSO) algorithm is used to solve the trajectory optimization model to obtain the optimal trajectory of the networking system. Each station radar moves according to the optimal trajectory, and according to the principle of the anti-deception interference method of the moving platform radar networking system, the Mahalanobis distance at each moment is calculated, and the Mahalanobis distances at multiple moments are accumulated as hypothesis test statistics to achieve true / false target identification.

[0098] Embodiment 2: Based on the method corresponding to the above embodiment 1, a simulation experiment is introduced.

[0099] Step 1: Take the two radars in the networking system as an example to simulate and verify the mathematical model of false target misjudgment probability of the two radars. The parameter settings of the two radars are shown in Table 1. Assume that the deception distance is 1000m, the x-axis range of the target detection area is (-100~150)km, the y-axis range is (-50~100)km, and the z-axis is 0.

[0100] The Monte Carlo simulation of the anti-deceptive jamming method of the moving platform radar networking system was performed 5000 times, and the statistical values ​​of the real target identification probability and the false target misjudgment probability of the networking system were obtained, which were compared with the theoretical results.

[0101] Select the significance level is 0.01, then the theoretical value of the true target identification probability is ; The theoretical result of false target misjudgment probability is given by The statistical and theoretical values ​​of the probability of identifying the real target are as follows: Figure 5-Figure 6 As shown in the figure, the statistical and theoretical values ​​of the false target misjudgment probability are as follows: Figure 7-Figure 8 As shown in the figure, the error between the statistical value of false target misjudgment probability and the theoretical value is as follows: Fig. 9 As shown in Table 1, the radar parameter table is shown in Table 1.

[0102] Table 1

[0103]

[0104] Step 2: Take the radar network system composed of three radars as an example. The target detection area D is a square area. The x-axis and y-axis ranges are both -2km to 2km, the z-axis coordinate is always 0, and the center of the detection area is the coordinate origin. 9 points are evenly taken in the detection area to calculate the target fitness value in the area. The deception distance is set to 300m. The radial distance measurement error of the three radars is set to 5m, the angle measurement error is 0.2°, and the position error of the radar in the x, y, and z coordinate axes is 100m. Assume that the three radars are at the same height of 30km.

[0105] The constraints at the first moment are different from those at the following four moments, and the optimization time at the first moment is not restricted, so the population size and iteration times in the PSO algorithm can be set larger. At the following four moments, the radar network needs to find the optimal position at the next moment during the movement, which requires the optimization speed of the algorithm, so the population size and iteration times at the following four moments need to be set relatively small.

[0106] Simulation scenario 1: Set the center point of the radar network location optimization area and the center point of the target detection area to coincide with the coordinate origin, such as Fig.10 The dark area indicates the optimal position search range of the radar networking system at the first moment. The PSO algorithm parameters and constraint parameter settings at the first moment are shown in Table 2. The PSO algorithm parameters and constraint parameter settings at the next four moments are shown in Table 3.

[0107] Table 2

[0108]

[0109] Table 3

[0110]

[0111] The results of radar network system trajectory optimization are as follows: Fig.11 As shown, the optimal objective function fitness value at each moment and the corresponding radar position coordinates are shown in Table 4.

[0112] Table 4

[0113]

[0114] Simulation scenario 2: Set the center point of the radar network location optimization area away from the center point of the target detection area, and the center point of the target detection area is still located at the origin of the coordinates. The optimized trajectories of the three radars are as follows: Fig.12 shown.

[0115] Step 3: Using the optimized trajectory in the simulation scenario 2 as the motion trajectory of the networked radar system, the true / false target identification effect of the present invention is compared with that of the existing method that does not consider the radar position.

[0116] The radial distance measurement error of the three radars is 5m, and the azimuth and elevation angle measurement errors are 0.2°. The deception distance is set to 500m, and the Monte Carlo simulation is performed 5000 times to analyze the impact of radar position error on the probability of true / false target identification of the proposed method. The simulation results are shown in Figure 2. Fig.13 shown.

[0117] The radar parameters remain unchanged, and Monte Carlo simulation is performed 5000 times. When the radar position error is set to 10m, 50m, and 100m respectively, the influence of different deception distances on the true / false target identification probability of the two methods is analyzed. The simulation results are shown in Figure 14-15 shown.

[0118] According to the simulation experiment, we know that:

[0119] Depend on Figure 5-Figure 6 It can be seen that the actual target identification probability statistics of the radar networking system are close to the theoretical value. Figure 7-Figure 9 It can be seen that the error between the statistical result of the false target misjudgment probability and the theoretical analysis result is relatively small, which proves the correctness of the mathematical model of the false target misjudgment probability of the radar networking system in the present invention.

[0120] Depend on Fig.13 It can be seen that as the radar position error increases, the probability of real target identification of the method of the present invention always remains at a high level, while the probability of false target identification gradually decreases. Therefore, selecting radars with smaller radar position errors for networking can effectively improve the false target identification probability of the networking system.

[0121] Depend on Fig.14 It can be seen from the analysis that as the deception distance increases, the real target identification probability of the present invention and the existing method remains relatively stable. However, as the radar position error increases, the real target identification probability of the present invention always remains at a high level, while the real target identification probability of the existing method is significantly reduced. This is because the existing method does not take into account the radar position error and mistakenly reduces the parameter value in the error covariance matrix in the anti-interference algorithm, so the Mahalanobis distance calculated after the inversion of the error covariance matrix increases inversely. Assuming that the test threshold remains unchanged, the test statistic of the increase in the Mahalanobis distance is difficult to pass the hypothesis test, resulting in the real target identification probability of the existing method gradually decreasing with the increase of the radar position error.

[0122] Depend on Fig.15It can be seen from the analysis that with the increase of deception distance, the false target identification probability of both methods gradually increases. However, the false target identification probability of the present invention gradually decreases with the increase of radar position error, while that of the existing method gradually increases. This is also the result of the chi-square test of the radar position error affecting the test statistic. It is worth noting that in the process of distinguishing true and false targets, it is meaningful to distinguish false targets only when the real targets are not eliminated. Although the false target identification effect of the existing method is better than the effect of the present invention after considering the radar position error, the price paid by the real target identification probability of the existing method is too high.

[0123] The present invention can be used in the radar networking system of a moving platform to resist the interference of distance deception type false targets based on trajectory optimization. Taking minimizing the false target misjudgment probability of the networking system as the objective function, the constraints of the movement of the networking system are reasonably set to solve the optimal movement trajectory of the radar networking system. Based on this trajectory, the anti-deception interference method of the radar networking of the moving platform is used to achieve effective identification of distance deception type false targets while ensuring a high probability of true identification of real targets. The anti-interference method of the radar networking system of the moving platform considering the radar position error, the mathematical model of the false target misjudgment probability of the radar networking system of the moving platform and the trajectory optimization model of the radar networking system of the moving platform in the present invention should belong to the protection scope of the claims attached to the present invention.

[0124] Embodiment 3: In order to execute the method corresponding to the above embodiment 1 to achieve the corresponding functions and technical effects, a radar networking anti-deception interference system based on trajectory optimization is provided below.

[0125] A radar network anti-deception jamming system based on trajectory optimization, comprising:

[0126] A false target misjudgment probability mathematical model establishment module is used to establish a false target misjudgment probability mathematical model of the moving platform radar networking system based on the moving platform radar networking system anti-distance deception interference method; the moving platform radar networking system anti-distance deception interference method takes into account the radar position error.

[0127] The trajectory optimization model construction module is used to set the constraint conditions of the moving platform radar networking system based on the false target misjudgment probability mathematical model and take minimizing the false target misjudgment probability as the objective function to construct the trajectory optimization model of the moving platform radar networking system.

[0128] The optimal trajectory determination module is used to determine the optimal trajectory of each radar in the moving platform radar networking system according to the trajectory optimization model. The radar networking system moves according to the optimal trajectory and uses the moving platform radar networking anti-distance deception interference method to identify true / false targets.

[0129] Embodiment 4: An electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program so that the electronic device executes the radar networking anti-deception interference method based on trajectory optimization described in Embodiment 1.

[0130] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the radar networking anti-deception interference method based on trajectory optimization described in Example 1.

[0131] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0132] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A radar network anti-deception interference method based on trajectory optimization, characterized in that: include: Based on the anti-distance deception jamming method of the moving platform radar networking system, a mathematical model of false target misjudgment probability of the moving platform radar networking system is established; the anti-distance deception jamming method of the moving platform radar networking system takes into account the radar position error; Based on the false target misjudgment probability mathematical model, taking minimizing the false target misjudgment probability as the objective function, setting the constraint conditions of the moving platform radar networking system, and constructing the trajectory optimization model of the moving platform radar networking system; The objective function of the trajectory optimization model is: in, is the position vector of the i-th radar at the k-th moment; D is the detection area; k is any moment; K is the total time for false target identification; P ij represents the false target misjudgment probability of the i-th radar and the j-th radar; N is the number of radars in the mobile platform radar networking system; The constraints of the motion of the moving platform radar networking system in the trajectory optimization model include the constraints at the first moment and the constraints at the following moments; The constraints at the first moment are described as follows: Among them, R1 is the minimum radius of the optimization range; R2 is the maximum radius of the optimization range; ΔR min is the minimum distance between two radars; is the position vector of the i-th radar at the first moment; X T is the coordinate vector of the center point of the detection area; is the position vector of the jth radar at the first moment; The constraints at the next few moments are described as follows: in, is the position vector of the i-th radar at the k-th moment; is the position vector of the jth radar at the kth moment; Indicates the distance between the radar and the center of the target detection area at the kth moment; Indicates the distance between the radar's optimal position at the last moment and the center of the target detection area; Δr max It indicates the maximum radial distance change of the radar moving toward the target area based on the optimal position at the last moment; Δrmin indicates the minimum radial distance change of the radar moving toward the target area based on the optimal position at the last moment; represents the azimuth of the radar at the kth moment in the rectangular coordinate system with the center point of the target area as the origin; represents the azimuth of the radar's optimal position at the last moment; Δθ represents the maximum angle at which the radar platform is allowed to maneuver; The optimal trajectory of each radar in the moving platform radar networking system is determined according to the trajectory optimization model, and the anti-deception interference method of the moving platform radar networking system is used to identify real targets and false targets.

2. The radar network anti-deception interference method based on trajectory optimization according to claim 1 is characterized in that: The anti-distance deception interference method of the moving platform radar networking system specifically includes: Obtain target quantity measurements of any two radars in the moving platform radar networking system, and convert the target quantity measurements into a unified rectangular coordinate system; Acquire the measurement state of the target quantity measurement in the unified rectangular coordinate system, and calculate the error covariance matrix of the corresponding target in the radar according to the target quantity measurement in the radar and the measurement error of the radar; Calculate the Mahalanobis distance between two targets according to the measurement state and the corresponding error covariance matrix; the Mahalanobis distance is used as a hypothesis test statistic for false target identification; Testing and identifying distance deceptive false targets according to the hypothesis test statistic; False target identification is performed on radars in the moving platform radar networking system in pairs, and all targets of all radars in the moving platform radar networking system are traversed. If and only if all combined judgment results about the same target in all radars are judged to be true targets, the target is judged to be a true target by the moving platform radar networking system, otherwise the target is judged to be a false target.

3. The radar network anti-deception interference method based on trajectory optimization according to claim 2 is characterized in that: The measurement status is: in, is the x-axis value of the target measurement value in a unified rectangular coordinate system; is the y-axis value of the target measurement value in the unified rectangular coordinate system; R i is the length of the projection of the target radial distance on the xoy plane of the unified rectangular coordinate system, r i is the radial distance of the target measured by the i-th radar, is the target elevation angle measured by the i-th radar; θ i is the target azimuth measured by the i-th radar; (xi,yi) represents the spatial position of the i-th radar in the unified rectangular coordinate system.

4. The radar network anti-deception interference method based on trajectory optimization according to claim 3 is characterized in that: Calculating the Mahalanobis distance between two targets according to the measurement state and the corresponding error covariance matrix specifically includes: Constructing a diagonal matrix consisting of the measurement error and the radar position error; constructing an error covariance matrix based on a diagonal matrix consisting of the measurement error and the radar position error; Determine the coordinate state difference of two targets in the xoy plane according to the measurement state; The Mahalanobis distance between two targets measured by two radars is calculated according to the coordinate state difference and the error covariance matrix.

5. The radar network anti-deception interference method based on trajectory optimization according to claim 1 is characterized in that: Based on the anti-distance deception interference method of the moving platform radar networking system, a mathematical model of false target misjudgment probability of the moving platform radar networking system is established, which specifically includes: The two target quantities measured by the two radars are converted into a unified rectangular coordinate system, and the state coordinates of the two targets in the two radars in the xoy plane of the unified rectangular coordinate system are obtained; Determine the difference in state coordinates of two targets in two radars; For the distance deception false target, the Mahalanobis distance between the two targets in the two radars is calculated according to the state coordinate difference, and a two-dimensional probability density function of the state coordinate difference is determined; When the target state coordinates measured by the two radars correspond to a false target but are misjudged as a real target, the probability that the Mahalanobis distance is less than or equal to the chi-square distribution threshold value is defined as the false target misjudgment probability, and the false target misjudgment probability model is determined according to the double integral of the two-dimensional probability density function in the region where the Mahalanobis distance is less than or equal to the threshold value; According to the false target misjudgment probability models of the two radars, a false target misjudgment probability mathematical model of the entire moving platform radar networking system is constructed.

6. The radar network anti-deception interference method based on trajectory optimization according to claim 5 is characterized in that: The mathematical model P of false target misjudgment probability of the whole moving platform radar network system F 'for: Among them, P ij represents the false target misjudgment probability of the i-th radar and the j-th radar; N is the number of radars in the moving platform radar networking system.

7. A radar network anti-deception jamming system based on trajectory optimization, characterized in that: include: A false target misjudgment probability mathematical model establishment module is used to establish a false target misjudgment probability mathematical model of the moving platform radar networking system based on the anti-distance deception interference method of the moving platform radar networking system; the anti-distance deception interference method of the moving platform radar networking system takes into account the radar position error; A trajectory optimization model building module is used to set the constraint conditions of the moving platform radar networking system based on the false target misjudgment probability mathematical model, taking minimizing the false target misjudgment probability as the objective function, and building a trajectory optimization model of the moving platform radar networking system; The objective function of the trajectory optimization model is: in, is the position vector of the i-th radar at the k-th moment; D is the detection area; k is any moment; K is the total time for false target identification; P ij represents the false target misjudgment probability of the i-th radar and the j-th radar; N is the number of radars in the mobile platform radar networking system; The constraints of the motion of the moving platform radar networking system in the trajectory optimization model include the constraints at the first moment and the constraints at the following moments; The constraints at the first moment are described as follows: Among them, R1 is the minimum radius of the optimization range; R2 is the maximum radius of the optimization range; ΔR min is the minimum distance between two radars; is the position vector of the i-th radar at the first moment; X T is the coordinate vector of the center point of the detection area; is the position vector of the jth radar at the first moment; The constraints at the next few moments are described as follows: in, is the position vector of the i-th radar at the k-th moment; is the position vector of the jth radar at the kth moment; Indicates the distance between the radar and the center of the target detection area at the kth moment; Indicates the distance between the radar's optimal position at the last moment and the center of the target detection area; Δr max It indicates the maximum radial distance change of the radar moving toward the target area based on the optimal position at the last moment; Δrmin indicates the minimum radial distance change of the radar moving toward the target area based on the optimal position at the last moment; represents the azimuth of the radar at the kth moment in the rectangular coordinate system with the center point of the target area as the origin; represents the azimuth of the radar's optimal position at the last moment; Δθ represents the maximum angle at which the radar platform is allowed to maneuver; The optimal trajectory determination module is used to determine the optimal trajectory of each radar in the moving platform radar networking system according to the trajectory optimization model, and to identify real targets and false targets based on the optimal trajectory.

8. An electronic device, characterized in that: It includes a memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the radar networking anti-deception interference method based on trajectory optimization as described in any one of claims 1-6.

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