Method for identifying multi-missile collaborative active deception false targets based on active and passive radars

Abstract

A method for identifying true and false targets in multi-missile cooperative active deception false targets based on active and passive radars belongs to the field of radar true and false target identification. The present invention aims at the problem that in the identification of true and false targets in existing radar systems, it is impossible to identify true and false targets when cooperative false targets and non-cooperative false targets exist simultaneously. It includes determining first-level candidate true targets according to the range measurement and azimuth measurement of the target by the active radar in the common detection area of multiple missiles, and eliminating non-cooperative false targets; then determining second-level candidate true targets according to the radial velocity of the target collected by the active radar corresponding to the first-level candidate true targets in the common detection area of multiple missiles, and further eliminating non-cooperative false targets to determine the second-level position measurement association sequence; then performing measurement information fusion on the transformed measurements of the second-level candidate true targets to obtain the fused target position; finally, combining the azimuth measurement of the passive radar to determine the final true target and eliminating cooperative false targets. The present invention is used for the identification of true and false targets in multi-missile cooperation.

Description

Technical Field

[0001] The present invention relates to a method for identifying cooperative active decoy false targets based on active and passive radars, and belongs to the field of radar true and false target identification. Background Art

[0002] The essence of the problem of multi-missile cooperative anti-jamming is the anti-jamming problem of a multi-station radar system. A multi-station radar system refers to a radar system composed of multiple transmitting stations, receiving stations, or transmitting / receiving stations dispersed in a certain area. Inside the radar system, a communication network is built through various communication means, so that each radar station is directly or indirectly connected. The fusion center of the system collects the information (measurement information, echo information, etc.) of each radar station, and performs information fusion and joint processing.

[0003] In recent years, as two opposing technologies, electronic countermeasures and electronic counter-countermeasures in the radar field have continued to develop in mutual confrontation. Radar active jamming is mainly divided into two categories: suppression jamming and deception jamming. Among them, deception jamming is mainly applied to self-defense jamming and accompanying jamming, and has many advantages. It can effectively concentrate the jamming energy within the bandwidth of the radar receiver, and share the matched filtering gain of the radar, achieving a satisfactory jamming effect with a small power. It can simultaneously generate multiple active false targets, and is more suitable for jamming tracking radars.

[0004] In terms of electronic anti-jamming technology, it has gradually developed from single-station radar to multi-station radar. Technologies including using the kinematic characteristics of targets, based on polarization characteristics, and pulse diversity have all been used in the problem of single-station radar against active deception jamming. However, the single-station radar has a single perception dimension of the surrounding environment, and with the increase in the complexity of electronic jamming, the technology of multi-station radar systems against deception jamming has developed rapidly. The multi-station radar system can detect targets from multiple perspectives and multiple dimensions, and can use information fusion technology to obtain more accurate information about the targets.

[0005] Patent CN114924236A ("Air-ground Radar Cooperative Anti-deception Jamming Method Based on Position and Velocity Information") introduces an air-ground radar cooperative anti-deception jamming method based on position and velocity information, which performs track association tests based on position information and velocity information on the measured values of true and false targets obtained by the networked radar to achieve true and false target identification. This method only uses multi-base active radars and cannot identify non-cooperative false targets, and has a poor identification probability.

[0006] Patent CN114779186A ("Method for Anti-Spoofing Jamming of Multi-Station Radar System Based on Power Optimization") introduces a method for anti-spoofing jamming of a multi-station radar system based on power optimization. By optimizing the power of the signals transmitted by each radar transmitting station, transmitting the optimized signals, and then processing the echoes received by each radar receiving station to obtain the value of the spoofing distance Δd, the discrimination threshold between the true target and the spoofing false target is calculated. Finally, the false target is filtered out, and the true target is tracked using a tracking algorithm. This method improves the estimation accuracy of spoofing distance parameters and also improves the discrimination performance of the multi-station radar system against spoofing false targets. However, it may misidentify cooperative false targets as real targets, and the discrimination probability is poor.

[0007] Patent CN113484838A ("Method and System for Identifying Active False Targets of Multi-Base Radar") introduces a method and system for identifying active false targets under multiple interference sources. By calculating the correlation coefficient between every two targets, clustering analysis is performed on the targets, and then threshold detection is used to determine whether each target in the cluster is an active false target. This method can overcome the problem that the existing methods for anti-spoofing jamming of multi-base radar cannot be applied to multiple interference sources. However, it may cluster cooperative false targets and identify them as real targets, with a poor discrimination probability, and the discrimination performance degrades when there are position errors in the radar.

[0008] Patent CN113534067A ("Method and System for Anti-Spoofing Jamming of Multi-Station Radar under Multiple Interference Sources") introduces a method for anti-spoofing jamming of multi-station radar under multiple interference sources. By obtaining the received signal vectors of the targets detected by the distributed multi-station radar, calculating the correlation coefficient between different two targets, quantifying to obtain the correlation coefficient matrix between each target, and then comparing each element in the target number vector of each target with the discrimination threshold to determine whether each target is an active false target, anti-spoofing jamming is achieved. This method cannot distinguish between real targets and cooperative false targets, and the discrimination probability is poor.

[0009] Patent CN103728599A ("Method for Suppressing Spoofing False Target Interference with a Geographically Separated Active-Passive Radar Network") introduces a method for suppressing spoofing false target interference with a geographically separated active-passive radar network. By using the measurement values of the targets in the active-passive radar network, calculating the associated distances of each target of the active and passive radars at different times, further obtaining the discrimination statistic, and finally performing threshold testing to distinguish between true and false targets for each target of the active radar and eliminating false targets. This method reduces the false discrimination probability of false targets. However, when there are position errors in the radar, its discrimination performance is poor compared to the discrimination algorithm for compensating position errors. Summary of the Invention

[0010] Aiming at the problem that in the true and false target discrimination of existing radar systems, it is impossible to discriminate true and false targets when cooperative false targets and non-cooperative false targets exist simultaneously, the present invention provides a multi-missile cooperative active deception false target discrimination method based on active and passive radars.

[0011] A multi-missile cooperative active deception false target discrimination method based on active and passive radars of the present invention includes:

[0012] Step 1: Obtain the range measurement and azimuth measurement of the target by multiple active radars in the common detection area of multiple missiles, and convert them into a rectangular coordinate system to obtain the corresponding converted measurements; calculate the error of the converted measurements, and calculate the position error covariance matrix of the converted measurements;

[0013] Based on the position error covariance matrix of the converted measurements, calculate the position Mahalanobis distance of each combination of two converted measurements. The targets tracked by the active radars corresponding to the measurement data with all position Mahalanobis distances less than the position discrimination threshold are used as first-level candidate true targets, and the converted measurements of the active radars corresponding to the first-level candidate true targets form a primary position measurement association sequence; other targets are excluded as non-cooperative false targets;

[0014] Step 2: Obtain the radial velocity of the target collected by the active radars corresponding to the first-level candidate true targets in the common detection area of multiple missiles. Calculate the actual composite velocity of the current target from the radial velocities of the targets corresponding to every two active radars, form an actual composite velocity sequence, and calculate the velocity error covariance matrix sequence of the actual composite velocity sequence;

[0015] Based on the velocity error covariance matrix corresponding to the actual composite velocity, calculate the velocity Mahalanobis distance of every two actual composite velocities. The first-level candidate true targets tracked by the active radars corresponding to the target radial velocities with all velocity Mahalanobis distances less than the velocity discrimination threshold are used as second-level candidate true targets, and the corresponding converted measurements are retained in the primary position measurement association sequence to form a secondary position measurement association sequence; other first-level candidate true targets are further excluded as non-cooperative false targets;

[0016] Step 3: Perform measurement information fusion on all the converted measurements corresponding to each target in the secondary position measurement association sequence to obtain the fused target position;

[0017] Step 4: Convert each fused target position into the passive radar coordinate system to obtain the azimuth angle of the current target relative to the passive radar, and calculate the azimuth angle variance; then calculate the azimuth angle estimation error and azimuth angle estimation error variance, and calculate the azimuth angle discrimination statistic; The second-level candidate true targets tracked by the active radars corresponding to all azimuth angle discrimination statistics less than the azimuth angle discrimination threshold are used as real targets, and other second-level candidate true targets are excluded as cooperative false targets.

[0018] For the multi-missile collaborative active deception false target identification method based on active and passive radars according to the present invention, the method for obtaining the position error covariance matrix of the transformed measurement in step one includes:

[0019] Transform the range measurements and azimuth angle measurements of multiple active radars on the target after time alignment into the rectangular coordinate system to obtain the transformed measurement Z:

[0020] Z = [x t y t T ,

[0021] where x t is the abscissa of the transformed measurement, and y t is the ordinate of the transformed measurement:

[0022]

[0023] In the formula, x is the abscissa of the active radar position, y is the ordinate of the active radar position, r is the range measurement of the active radar on the target, and θ is the azimuth angle measurement of the active radar on the target;

[0024] Calculate the error dZ of the transformed measurement as:

[0025]

[0026] where Α is the first transformation matrix:

[0027]

[0028] The position error covariance matrix P of the transformed measurement is:

[0029] P = E[dZdZ T = AΛA T + Λ s ,

[0030] where E[·] is the mean operation;

[0031] Λ is the diagonal matrix of the active radar measurement error:

[0032] where σ r is the ranging error of the active radar, and σ θ is the angle measurement error of the active radar;

[0033] Λ s is the diagonal matrix of the active radar positioning error:

[0034] σ x is the positioning error of the active radar in the x direction, and σ yis the positioning error of the active radar in the y direction.

[0035] According to the multi-missile cooperative active deception false target identification method based on active and passive radars of the present invention, the method for obtaining the primary position measurement association sequence in step one includes:

[0036] Calculate the position Mahalanobis distance d of each two transformed measurement combinations ij :

[0037]

[0038] Σ ij = E[d(Z i - Z j )d(Z i - Z j ) T = P i + P j ,

[0039] where Z i is the transformed measurement of the i-th active radar, Z j is the transformed measurement of the j-th active radar, i, j = 1, 2, 3,..., n, n is the total number of active radars; i ≠ j;

[0040] Σ ij is the position error covariance matrix of Z i - Z j ; P i is the position error covariance matrix of the i-th active radar, P j is the position error covariance matrix of the j-th active radar;

[0041] Compare the position Mahalanobis distance d of each two transformed measurement combinations ij with the position discrimination threshold η r to identify whether the transformed measurement combination comes from the same real target:

[0042]

[0043] where H 0 indicates that the current transformed measurement combination comes from the same first-level candidate real target;

[0044] Form a sequence of the transformed measurements of the active radars corresponding to all those identified as coming from the same first-level candidate real target as the primary position measurement association sequence; the transformed measurements of other active radars are determined to come from non-cooperative false targets.

[0045] According to the multi-missile cooperative active deception false target identification method based on active and passive radars of the present invention, the method for obtaining the speed error covariance matrix of the actual synthetic speed in step two includes:

[0046] For the velocity measurement combination formed by the target radial velocities corresponding to every two active radars, the actual combined velocity v of the current target ij is:

[0047] v ij = [v x , v y T ,

[0048]

[0049] where v x is the velocity of the current target in the x direction, and v y is the velocity of the current target in the y direction; θ i is the azimuth measurement of the i-th active radar, and θ j is the azimuth measurement of the j-th active radar; v i is the velocity measurement of the i-th active radar, and v j is the velocity measurement of the j-th active radar;

[0050] The differential dv of the actual combined velocity v of the current target ij is: ij is:

[0051]

[0052] where B ij is the second transformation matrix:

[0053]

[0054] where α ij is an intermediate variable: α ij = sin(θ j - θ i );

[0055] β ij is an intermediate variable: β ij = v j - cos(θ i - θ j )v i ;

[0056] The velocity error covariance matrix P of the actual combined velocity v ij is: ij is:

[0057]

[0058] where Λ ij is the measurement error diagonal matrix of the i-th active radar and the j-th active radar: ​

[0059]

[0060] where σ θ,i is the angle measurement error of the i-th active radar, and σ θ,j is the angle measurement error of the j-th active radar, and σ v,i is the velocity measurement error of the i-th active radar, and σ v,j is the velocity measurement error of the j-th active radar.

[0061] According to the multi-missile cooperative active deception false target identification method based on active and passive radars of the present invention, the method for obtaining the secondary position measurement association sequence in step two includes:

[0062] Calculate the velocity Mahalanobis distance D for each combination of two velocity measurements:

[0063] D = (v ij - v i′j′ ) T X -1 (v ij - v i′j′ ),

[0064] i′, j′ = 1, 2, 3,..., n, i′ ≠ j′;

[0065] where X is the velocity error covariance matrix of v ij - v i′j′ :

[0066]

[0067] where

[0068]

[0069] Compare the velocity Mahalanobis distance D of each combination of two velocity measurements with the velocity discrimination threshold η v to identify whether each combination of two velocity measurements comes from the same real target:

[0070]

[0071] Retain the converted measurements of the active radars corresponding to all those identified as coming from the same secondary candidate real target in the primary position measurement association sequence to form the secondary position measurement association sequence; determine other primary candidate real targets as coming from non-cooperative false targets.

[0072] According to the multi-missile cooperative active deception false target identification method based on active and passive radars of the present invention, in step three, the fused target position Z fusion is:

[0073]

[0074] where Z fusion =[x fusion y fusion T , x fusion is the abscissa of the target position after fusion, and y fusion is the ordinate of the target position after fusion.

[0075] According to the multi-missile cooperative active deception false target identification method based on active and passive radars of the present invention, in step four, the azimuth angle of the current target relative to the passive radar is θ fusionToPas :

[0076]

[0077] where x pas is the abscissa of the passive radar position, and y pas is the ordinate of the passive radar position.

[0078] According to the multi-missile cooperative active deception false target identification method based on active and passive radars of the present invention, in step four, the method for calculating the azimuth angle variance includes:

[0079] Calculate the differential dθ fusionToPas of θ fusionToPas :

[0080]

[0081] where C θ is the third transformation matrix, and C s,θ is the compensation transformation matrix:

[0082]

[0083] C s,θ =-C θ ,

[0084] then the variance fusionToPas of the azimuth angle θ is:

[0085]

[0086] where P fusion is the covariance matrix of the target position error after fusion:

[0087]

[0088] Λ pas is the diagonal matrix of the passive radar positioning error:

[0089] ​

[0090] where σ x,pas is the positioning error of the passive radar in the x direction, and σ y,pas is the positioning error of the passive radar in the y direction.

[0091] According to the multi-missile cooperative active deception false target identification method based on active and passive radars of the present invention, in step four, the calculation method of the azimuth discrimination statistic includes:

[0092] Calculate the azimuth estimation error Δθ fusion and the variance of the azimuth estimation error

[0093] Δθ fusion = θ fusionToPas - θ Pas ,

[0094] where θ pas is the target azimuth angle obtained by the passive radar;

[0095]

[0096] In the formula is the angle measurement error of the passive radar;

[0097] Set the azimuth discrimination statistic as Δ θ :

[0098]

[0099] According to the multi-missile cooperative active deception false target identification method based on active and passive radars of the present invention, in step four,

[0100] Compare the azimuth discrimination statistic Δ θ of each target with the azimuth discrimination threshold η θ to identify whether the target position after fusion corresponds to the real target:

[0101]

[0102] Delete all the measurement data identified as cooperative false targets, and determine the final real target for continuous tracking.

[0103] The beneficial effects of the present invention: The present invention can achieve the identification of true and false targets in the presence of both cooperative and non-cooperative false targets, and can eliminate the influence of the radar's own positioning error on the identification effect.

[0104] The method of the present invention first identifies and eliminates non-cooperative false targets by converting positions, then further eliminates non-cooperative false targets by synthesizing speeds, and finally fuses the measurement information of the secondary candidate true targets, combines the passive radar measurement information, and further identifies cooperative false targets and real targets; the method of the present invention takes into account the positioning error of the radar itself, is more in line with the actual battlefield situation, and improves the probability of identifying true and false targets in the presence of radar positioning errors, making the identification result of true and false targets more reliable, so as to meet the requirements of multi-missile system cooperative anti-jamming. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] Figure 1 is a flowchart of the multi-missile cooperative active deception false target identification method based on active and passive radars according to the present invention;

[0106] Figure 2 is a schematic diagram of a multi-missile cooperative anti-jamming system based on active / passive radars; in the figure, T represents transmission and R represents reception; in the figure, the main missile corresponds to the active radar and the accompanying missile corresponds to the passive radar. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0107] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0108] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other.

[0109] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but it is not limited to the present invention.

[0110] DETAILED DESCRIPTION OF THE EMBODIMENTS I. Combining Figure 1 and Figure 2 as shown, the present invention provides a multi-missile cooperative active deception false target identification method based on active and passive radars, including,

[0111] Step 1: Obtain the range measurement and azimuth measurement of the target by multiple active radars in the common detection area of multiple missiles, and convert them into a rectangular coordinate system to obtain the corresponding converted measurements; calculate the error of the converted measurements, and calculate the position error covariance matrix of the converted measurements;

[0112] Calculate the position Mahalanobis distance for each combination of two transformed measurements based on the position error covariance matrix of the transformed measurements. Targets tracked by the active radar corresponding to the measurement data when all position Mahalanobis distances are less than the position discrimination threshold are regarded as first-level candidate true targets, and the transformed measurements of the active radar corresponding to the first-level candidate true targets form a primary position measurement association sequence; other targets are excluded as non-cooperative false targets.

[0113] Step 2: Obtain the target radial velocity collected by the active radar corresponding to the first-level candidate true targets within the common detection area of multiple missiles. Calculate the actual composite velocity of the current target from the target radial velocities corresponding to every two active radars to form an actual composite velocity sequence, and calculate the velocity error covariance matrix sequence of the actual composite velocity sequence.

[0114] Calculate the velocity Mahalanobis distance for each combination of two actual composite velocities based on the velocity error covariance matrix corresponding to the actual composite velocity. Targets tracked by the active radar corresponding to the target radial velocities when all velocity Mahalanobis distances are less than the velocity discrimination threshold are regarded as second-level candidate true targets, and the corresponding transformed measurements are retained in the primary position measurement association sequence to form a secondary position measurement association sequence; other first-level candidate true targets are further excluded as non-cooperative false targets.

[0115] Step 3: Perform measurement information fusion on all the transformed measurements corresponding to each target in the secondary position measurement association sequence to obtain the fused target position.

[0116] Step 4: Transform each fused target position into the passive radar coordinate system to obtain the azimuth angle of the current target relative to the passive radar, and calculate the azimuth angle variance; then calculate the azimuth angle estimation error and the azimuth angle estimation error variance, and calculate the azimuth angle discrimination statistic; Targets tracked by the active radar when all azimuth angle discrimination statistics are less than the azimuth angle discrimination threshold are regarded as true targets, and other second-level candidate true targets are excluded as cooperative false targets.

[0117] The technical problems to be solved by this embodiment include:

[0118] 1) In the real battlefield, non-cooperative false targets and cooperative false targets often exist simultaneously, resulting in an increased probability of misjudging false targets, and further leading to a decline in the performance of the multi-missile system in hitting targets.

[0119] 2) The self-positioning error of the radar will reduce the performance of the true and false target discrimination method, making it difficult to meet the requirements of the multi-missile system for cooperative anti-interference.

[0120] Based on this, this embodiment uses the position and velocity of the active radar and the azimuth angle of the passive radar to jointly identify the authenticity of the target, eliminate the active false target, improve the probability of authenticating the true and false targets, make the authentication result of the true and false targets more reliable, and meet the requirements of multi-missile system cooperative anti-jamming.

[0121] Further, the method for obtaining the position error covariance matrix of the transformed measurement in step one includes:

[0122] Transform the range measurement and azimuth measurement of the target by multiple active radars after time alignment into the rectangular coordinate system to obtain the transformed measurement Z:

[0123] Z = [x t y t T ,

[0124] where x t is the abscissa of the transformed measurement, and y t is the ordinate of the transformed measurement:

[0125]

[0126] In the formula, x is the abscissa of the active radar position, y is the ordinate of the active radar position, r is the range measurement of the active radar to the target, and θ is the azimuth measurement of the active radar to the target;

[0127] Calculate the error dZ of the transformed measurement as:

[0128]

[0129] In the formula, d(·) is the differential symbol, is the partial derivative symbol;

[0130] In the formula, Α is the first transformation matrix:

[0131]

[0132] The position error covariance matrix P of the transformed measurement is:

[0133] P = E[dZdZ T = AΛA T +Λ s ,

[0134] In the formula, E[·] is the mean operation;

[0135] Λ is the diagonal matrix of the active radar measurement error:

[0136] In the formula, σ r is the ranging error of the active radar, and σ θThe angle measurement error of the active radar;

[0137] Λ s The diagonal matrix of the positioning error of the active radar:

[0138] σ x is the positioning error of the active radar in the x direction, and σ y is the positioning error of the active radar in the y direction.

[0139] In step one, the method of pairwise combining the conversion measurements from different active radars to obtain the primary position measurement association sequence includes:

[0140] Calculate the position Mahalanobis distance d ij :

[0141]

[0142] Σ ij = E[d(Z i - Z j )d(Z i - Z j ) T = P i + P j ,

[0143] In the formula, Z i is the conversion measurement of the i-th active radar, and Z j is the conversion measurement of the j-th active radar, where i, j = 1, 2, 3,..., n, and n is the total number of active radars; i ≠ j;

[0144] Σ ij is the position error covariance matrix of Z i - Z j ; P i is the position error covariance matrix of the i-th active radar, and P j is the position error covariance matrix of the j-th active radar;

[0145] Compare the position Mahalanobis distance d ij of each pair of conversion measurement combinations with the position discrimination threshold η r to identify whether the conversion measurement combination comes from the same true target:

[0146]

[0147] In the formula, H 0 indicates that the current conversion measurement combination comes from the same first-level candidate true target;

[0148] When the conversion measurements corresponding to all the active radars identified as coming from the same first-level candidate true target are formed into a sequence, it serves as the primary position measurement association sequence; the conversion measurements of other active radars are determined to come from non-cooperative false targets.

[0149] Taking the case of using three active radars as an example:

[0150] For a certain conversion measurement combination {Z 1 , Z 2 , Z 3}, if for any i, j (i, j = 1, 2, 3), H 0 all holds, then this measurement combination is added to the position measurement association sequence. The measurements not in the position measurement association sequence are determined to come from non-cooperative false targets.

[0151] Furthermore, if the measurements of two radars come from the same true target, the actual combined velocity v ij of this target can be obtained; the method for obtaining the velocity error covariance matrix of the actual combined velocity in step two includes:

[0152] For the velocity measurement combination composed of the target radial velocities corresponding to every two active radars, the actual combined velocity v ij of the current target is:

[0153] v ij = [v x , v y T ,

[0154]

[0155] In the formula, v x is the velocity of the current target in the x direction, v y is the velocity of the current target in the y direction; θ i is the azimuth measurement of the i-th active radar, θ j is the azimuth measurement of the j-th active radar; v i is the velocity measurement of the i-th active radar, v j is the velocity measurement of the j-th active radar; (r j , θ j , v j ) is the measurement of active radar j;

[0156] The differential dv ij of the actual combined velocity v ij of the current target is:

[0157]

[0158] In the formula, B ij is the second conversion matrix:​

[0159]

[0160] where α ij is an intermediate variable: α ij = sin(θ j - θ i );

[0161] β ij is an intermediate variable: β ij = v j - cos(θ i - θ j ) v i ;

[0162] The velocity error covariance matrix P ij of the actual synthesis velocity v ij is:

[0163]

[0164] where Λ ij is the measurement error diagonal matrix of the i-th active radar and the j-th active radar:

[0165]

[0166] where σ θ,i is the angle measurement error of the i-th active radar, σ θ,j is the angle measurement error of the j-th active radar, σ v,i is the velocity measurement error of the i-th active radar, σ v,j is the velocity measurement error of the j-th active radar.

[0167] The method for obtaining the secondary position measurement association sequence in step two includes:

[0168] Calculating the velocity Mahalanobis distance D for each combination of two velocity measurements:

[0169] D = (v ij - v i′j′ ) T X -1 (v ij - v i′j′ ),

[0170] i′, j′ = 1, 2, 3,..., n, i′ ≠ j′;

[0171] where X is the velocity error covariance matrix of v ij - v i′j′ :

[0172]

[0173] In the formula

[0174]

[0175] Compare the Mahalanobis distance D of each pair of velocity measurement combinations with the velocity discrimination threshold η v to determine whether each pair of velocity measurement combinations comes from the same true target:

[0176]

[0177] In the primary position measurement association sequence, retain the conversion measurements of the active radar corresponding to all pairs that are identified as coming from the same secondary candidate true target to form a secondary position measurement association sequence; determine other primary candidate true targets as coming from non-cooperating false targets.

[0178] Taking three active radars as an example, assume that a certain combination {Z 1 , Z 2 , Z 3} is obtained in the primary position measurement association sequence. Using the corresponding radial velocity combination {v 1 , v 2 , v 3} of {Z 1 , Z 2 , Z 3}, calculate v 12 and v 23 respectively, and calculate the Mahalanobis distance D between them, that is, the velocity discrimination statistic. The expression of D is as follows:

[0179] D = (v 12 - v 23 ) T X -1 (v 12 - v 23 ),

[0180]

[0181] where

[0182]

[0183]

[0184] where: X is the error covariance matrix of v 12 - v 23 ; P 12 and P 23 are the error covariance matrices of v 12 and v 23 ; B 12 and B23 For v 12 and v 23 conversion matrix; Σ is an intermediate variable, σ θ,2 is the angle measurement error of the second active radar, σ v,2 is the speed measurement error of the second active radar.

[0185] Furthermore, in step three, the fused target position Z fusion is:

[0186]

[0187] In the formula, Z fusion = [x fusion y fusion T , x fusion is the abscissa of the fused target position, y fusion is the ordinate of the fused target position.

[0188] Furthermore, in step four, the azimuth angle of the current target relative to the passive radar is θ fusionToPas :

[0189]

[0190] In the formula, x pas is the abscissa of the passive radar position, y pas is the ordinate of the passive radar position.

[0191] In step four, the method for calculating the azimuth variance includes:

[0192] Calculate the differential dθ fusionToPas of θ fusionToPas :

[0193]

[0194] In the formula, C θ is the third conversion matrix, C s,θ is the compensation conversion matrix:

[0195]

[0196] C s,θ = -C θ ,

[0197] Then the variance fusionToPas of the azimuth angle θ is:

[0198]

[0199] In the formula, P fusion ​is the target position error covariance matrix after fusion:

[0200]

[0201] Λ pas is the diagonal matrix of passive radar positioning error:

[0202]

[0203] where σ x,pas is the positioning error of the passive radar in the x direction, and σ y,pas is the positioning error of the passive radar in the y direction.

[0204] In step four, the calculation method of the azimuth discrimination statistic includes:

[0205] Calculate the azimuth estimation error Δθ fusion and the variance of the azimuth estimation error

[0206] Δθ fusion = θ fusionToPas - θ Pas ,

[0207] where θ pas is the target azimuth obtained by the passive radar;

[0208]

[0209] In the formula is the angle measurement error of the passive radar;

[0210] Set the azimuth discrimination statistic to Δ θ :

[0211]

[0212] In step four, compare the azimuth discrimination statistic Δ θ of each target with the azimuth discrimination threshold η θ to identify whether the target position after fusion corresponds to the real target:

[0213]

[0214] Delete all the measurement data identified as cooperative false targets, and determine the final real targets for continuous tracking.

[0215] While the invention has been described herein with reference to particular embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the invention. It should therefore be understood that numerous modifications may be made to the exemplary embodiments, and other arrangements may be devised, without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that the different dependent claims and the features described herein may be combined in ways different from those described in the original claims. It should also be understood that the features described in connection with individual embodiments may be used in other described embodiments.

Claims

1. A method for identifying multi-missile cooperative active deception false targets based on active and passive radars, characterized in that it includes Step 1: Obtain the range measurements and azimuth angle measurements of the targets by multiple active radars in the common detection area of multiple missiles, and convert them into a rectangular coordinate system to obtain the corresponding converted measurements; calculate the errors of the converted measurements, and calculate the position error covariance matrix of the converted measurements; Calculate the position Mahalanobis distance of each combination of two converted measurements based on the position error covariance matrix of the converted measurements. The targets tracked by the active radars corresponding to the measurement data with all position Mahalanobis distances less than the position discrimination threshold are used as first-level candidate true targets, and the converted measurements of the active radars corresponding to the first-level candidate true targets form a primary position measurement association sequence; other targets are excluded as non-cooperative false targets; Step 2: Obtain the radial velocity of the targets collected by the active radars corresponding to the first-level candidate true targets in the common detection area of multiple missiles. Calculate the actual synthetic velocity of the current targets from the radial velocities of the targets corresponding to every two active radars, form an actual synthetic velocity sequence, and calculate the velocity error covariance matrix sequence of the actual synthetic velocity sequence; Calculate the velocity Mahalanobis distance of every two actual synthetic velocities based on the velocity error covariance matrix corresponding to the actual synthetic velocity. The first-level candidate true targets tracked by the active radars corresponding to the target radial velocities when all velocity Mahalanobis distances are less than the velocity discrimination threshold are used as second-level candidate true targets, and the corresponding converted measurements are retained in the primary position measurement association sequence to form a secondary position measurement association sequence; other first-level candidate true targets are further excluded as non-cooperative false targets; Step 3: Perform measurement information fusion on all the converted measurements corresponding to each target in the secondary position measurement association sequence to obtain the fused target position; Step 4: Convert each fused target position into the passive radar coordinate system to obtain the azimuth angle of the current target relative to the passive radar, and calculate the azimuth angle variance; Then calculate the azimuth angle estimation error and the azimuth angle estimation error variance, and calculate the azimuth angle discrimination statistic; The second-level candidate true targets tracked by the active radars corresponding to all azimuth angle discrimination statistics less than the azimuth angle discrimination threshold are used as true targets, and other second-level candidate true targets are excluded as cooperative false targets.

2. The method for identifying multi-missile cooperative active deception false targets based on active and passive radars according to claim 1, characterized in that The method for obtaining the position error covariance matrix of the converted measurements in Step 1 includes: Convert the range measurements and azimuth angle measurements of the targets by multiple active radars after time alignment into a rectangular coordinate system to obtain the converted measurement Z: Z = [x t y t T ,​ where x t is the abscissa of the conversion measurement, and y t is the ordinate of the conversion measurement: x t = x + r cos θ y t = y + rsinθ, where x is the abscissa of the position of the active radar, y is the ordinate of the position of the active radar, r is the range measurement of the active radar on the target, and θ is the azimuth angle measurement of the active radar on the target; Calculate the error dZ of the converted measurement as: where Α is the first conversion matrix: The position error covariance matrix P of the converted measurement is: P = E[dZdZ T = AΛA T +Λ s , where E[·] is the operation of taking the mean; Λ is the diagonal matrix of active radar measurement errors: where σ r is the ranging error of the active radar, and σ θ is the angle measurement error of the active radar; Λ s is the diagonal matrix of the active radar positioning error: σ x is the positioning error of the active radar in the x direction, and σ y is the positioning error of the active radar in the y direction.

3. The method for identifying multi-missile cooperative active deception false targets based on active and passive radars according to claim 2, characterized in that The method for obtaining the primary position measurement correlation sequence in Step 1 includes: Calculate the position Mahalanobis distance d of each pair of transformed measurement combinations ij : Σ ij = E[d(Z i - Z j )d(Z i - Z j ) T = P i + P j , where Z i is the converted measurement of the i-th active radar, and Z j is the converted measurement of the j-th active radar, where i, j = 1, 2, 3, …, n, and n is the total number of active radars; i ≠ j; Σ ij is the position error covariance matrix of Z i -Z j ; P i is the position error covariance matrix of the i-th active radar, P j is the position error covariance matrix of the j-th active radar; The Mahalanobis distance d of the positions of every two converted measurement combinations ij is compared with the position discrimination threshold η r to determine whether the converted measurement combinations are from the same true target: where H 0 indicates that the current converted measurement combination comes from the same first-level candidate true target; Form a sequence with the converted measurements of the active radars corresponding to all the identified same first-level candidate true targets as the primary position measurement correlation sequence; the converted measurements of other active radars are determined to come from non-cooperative false targets.

4. The method for identifying non-cooperative false targets in multi-missile cooperative active deception based on active and passive radars according to Claim 3, characterized in that the method for obtaining the velocity error covariance matrix of the actual synthetic velocity in Step 2 includes: For each velocity measurement combination composed of the target radial velocities corresponding to every two active radars, the actual composite velocity v of the current target ij is as follows: v ij = [v x , v y T ,​ where v x is the velocity in the x - direction of the current target, and v y is the velocity in the y - direction of the current target; θ i is the azimuth measurement of the i - th active radar, and θ j is the azimuth measurement of the j - th active radar; v i is the velocity measurement of the i - th active radar, and v j is the velocity measurement of the j - th active radar; The differential dv of the actual synthesis speed v of the current target ij is as follows: ij For: where B ij is the second conversion matrix: where α ij is an intermediate variable: α ij = sin(θ j - θ i )); β ij is an intermediate variable: β ij = v j - cos(θ i - θ j ) v i ; Actual synthesis speed v ij Velocity error covariance matrix P ij is as follows: where Λ ij is the measurement error diagonal matrix between the i-th active radar and the j-th active radar: where σ θ,i is the angle measurement error of the i-th active radar, and σ θ,j is the angle measurement error of the j-th active radar, and σ v,i is the velocity measurement error of the i-th active radar, and σ v,j is the velocity measurement error of the j-th active radar.

5. The method for identifying non-cooperative false targets in multi-missile cooperative active deception based on active and passive radars according to Claim 4, characterized in that the method for obtaining the secondary position measurement correlation sequence in Step 2 includes: Calculate the velocity Mahalanobis distance D for each combination of two velocity measurements: D = (v ij - v i′j′ ) T X -1 (v ij - v i′j′ ), i′, j′ = 1, 2, 3, …, n, i′ ≠ j′; where X is the velocity ij -v i′j′ speed error covariance matrix: In the formula Compare the velocity Mahalanobis distance D of each pair of velocity measurements with the velocity discrimination threshold η v to determine whether each pair of velocity measurements comes from the same true target: Retain the converted measurements of the active radars corresponding to all the identified same second-level candidate true targets in the primary position measurement correlation sequence to form the secondary position measurement correlation sequence; other first-level candidate true targets are determined to come from non-cooperative false targets.

6. The method for identifying non-cooperative false targets in multi-missile cooperative active deception based on active and passive radars according to Claim 5, characterized in that In step 3, the target position Z after fusion fusion is as follows: where Z fusion = [x fusion y fusion T , x fusion is the abscissa of the target position after fusion, and y fusion is the ordinate of the target position after fusion.​ 7. The method for identifying non-cooperative false targets in multi-missile cooperative active deception based on active and passive radars according to Claim 6, characterized in that In step 4, the azimuth angle of the current target relative to the passive radar is θ fusionToPas : where x pas is the abscissa of the passive radar position, and y pas is the ordinate of the passive radar position.

8. The method for identifying non-cooperative false targets in multi-missile cooperative active deception based on active and passive radars according to Claim 7, characterized in that In Step 4, the method for calculating the azimuth variance includes: Calculate θ fusionToPas for the differential dθ fusionToPas : where C θ is the third conversion matrix, and C s,θ is the compensation conversion matrix: C s,θ = -C θ , Then the azimuth angle is θ fusionToPas The variance of is as follows: where P fusion is the target position error covariance matrix after fusion: Λ pas is the diagonal matrix of passive radar positioning error: where σ x,pas is the positioning error of the passive radar in the x-direction, and σ y,pas is the positioning error of the passive radar in the y-direction.

9. The method for identifying non-cooperative false targets in multi-missile cooperative active deception based on active and passive radars according to Claim 8, characterized in that In Step 4, the method for calculating the azimuth discrimination statistic includes: Calculate the azimuth estimation error Δθ fusion and the variance of the azimuth estimation error Δθ fusion = θ fusionToPas - θ Pas , where θ pas is the target azimuth angle obtained by the passive radar; where is the angle measurement error of the passive radar; Set the azimuth discrimination statistic to Δ θ :

10. The method for identifying non-cooperative false targets in multi-missile cooperative active deception based on active and passive radars according to Claim 9, characterized in that In Step 4, The azimuth discrimination statistic for each target is Δ θ is compared with the azimuth discrimination threshold η θ to determine whether the target position after fusion corresponds to the true target: Delete all the measurement data identified as cooperative false targets and determine the final true target for continuous tracking.

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