Distance-speed deception jamming identification method based on space-time-frequency feature fusion

Abstract

The invention discloses a distance-speed deception jamming identification method based on space-time-frequency feature fusion. The method comprises the following steps: radar signal spatial domain feature statistics are selected; radar signal frequency domain feature statistics are selected; radar signal time-space domain feature statistics are constructed; constructing time-frequency domain characteristic statistics of the radar signals; and radar signal space-time frequency fusion feature judgment. According to the method, the condition that a radar net is subjected to distance-speed composite deception jamming is taken as a research background, and after possible target tracks are formed for echo trace points by utilizing the characteristic differences of a real target and a false target in a space domain, a frequency domain and a time domain, the space domain characteristics and the frequency domain characteristics of the echo trace points at a single moment are extracted; and over-threshold detection is carried out on the statistics corresponding to the spatial domain features and the frequency domain features at each moment, and the detection result is represented by '0' or '1'. And finally, the detection results are accumulated at multiple moments, so that the judgment on whether the radar is subjected to distance-speed composite deception jamming or not is completed.

Description

Technical Field

[0001] The invention relates to the field of radar and electronic countermeasures, and in particular to a distance-speed deception interference identification method based on time-space-frequency feature fusion. Background Art

[0002] Distance deception jamming and speed deception jamming are generally active jamming implemented against pulse radar or pulse Doppler (PD) radar. Since most PD radars have the ability to measure both distance and speed, it is difficult to achieve the desired jamming purpose by using only a single deception method. Distance-speed combined deception jamming can simultaneously perform drag jamming on distance and speed information. It is difficult for the radar to identify whether it is a radar echo or an interference echo, which seriously affects the performance of the radar. Therefore, it is of great significance to adopt effective methods to counter composite deception jamming.

[0003] There are several existing identification methods for distance-velocity composite deception jamming: a jamming identification method based on a combination of orthogonal matching identification and extended Kalman filtering, a jamming identification method based on radial network extraction of Doppler feature differences, and a low observable target tracking method based on radar networking. However, all of the above methods use a single feature to identify distance-velocity composite deception jamming. When the jamming density increases, using a single feature is likely to cause misjudgment of the jamming pattern. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a distance-speed deception interference identification method based on time-space-frequency feature fusion, which utilizes the feature differences between real targets and false targets in the spatial domain, frequency domain and time domain, and adopts a variety of feature fusion methods to further reduce the probability of misjudgment of distance-speed composite deception interference on the basis of improving the probability of deception interference identification.

[0005] The object of the present invention is achieved through the following technical scheme: a distance-speed deception interference identification method based on time-space-frequency feature fusion, comprising the following steps:

[0006] Step 1: Selecting spatial feature statistics of radar signals;

[0007] Step 1.1, obtain the target's spatial position estimation coordinates and position error covariance matrix based on the radar's state estimation of the target at a certain moment;

[0008] Step 1.2, constructing the spatial feature statistics of the radar signal according to the target's spatial position estimation coordinates and position error covariance matrix obtained in step 1.1;

[0009] Step 2: Selecting frequency domain characteristic statistics of radar signals;

[0010] Step 2.1, obtaining the radial velocity value of the target according to the radar state estimation of the target at a certain moment;

[0011] Step 2.2, constructing the frequency domain characteristic statistics of the radar signal according to the radial velocity value of the target obtained in step 2.1;

[0012] Step 3: constructing the time-space domain characteristic statistics of radar signals;

[0013] Step 3.1, construct single-time spatial domain feature statistics;

[0014] Step 3.2, constructing the time-frequency domain statistics according to the single-time spatial domain feature statistics constructed in step 3.1;

[0015] Step 4: constructing the time-frequency domain characteristic statistics of radar signals;

[0016] Step 4.1, construct single-time frequency domain feature statistics;

[0017] Step 4.2, constructing time-frequency domain statistics according to the single-time frequency domain feature statistics constructed in step 4.1;

[0018] Step 5: Determine the time-space-frequency fusion characteristics of radar signals;

[0019] Step 5.1, construct the statistics corresponding to the time-space-frequency fusion features;

[0020] Step 5.2: Based on the statistics corresponding to the time-space-frequency fusion features constructed in step 5.1, make a distance-speed composite deception interference judgment.

[0021] Preferably, in step 1.1, the target's spatial position estimation coordinates and position error covariance matrix are obtained by the following formula:

[0022] Assume that at time k, radar l obtains the state estimation vector of target i through Kalman filtering: for:

[0023]

[0024] In the formula, It means that at time k, radar l obtains the coordinate of target i on the x-axis through Kalman filtering. It means that at time k, radar l obtains the speed of target i on the x-axis through Kalman filtering. It means that at time k, radar l obtains the coordinate of target i on the y-axis through Kalman filtering. It means that at time k, radar l obtains the speed of target i on the y-axis through Kalman filtering. It means that at time k, radar l obtains the coordinate of target i on the z-axis through Kalman filtering. It means that at time k, radar l obtains the speed of target i on the z-axis through Kalman filtering;

[0025] The spatial position coordinates of the target are estimated by the state estimation vector of the lth radar for:

[0026]

[0027] In the formula, It means that at time k, radar l obtains the coordinate of target i on the x-axis through Kalman filtering. It means that at time k, radar l obtains the coordinate of target i on the y-axis through Kalman filtering. It means that at time k, radar l obtains the coordinate of target i on the z-axis through Kalman filtering;

[0028] The position error covariance matrix of radar l to target i at time k is:

[0029] Preferably, in step 1.2, the spatial domain feature statistics of the radar signal are as follows:

[0030]

[0031] In the formula, is the state estimation covariance matrix of radar 1 for target i at time k; is the covariance matrix of radar 2’s state estimation of target i at time k; the eigenvalue η1(k) obeys the χ2 with 3 degrees of freedom. 2 distribution, that is, η1(k)~χ 2 (3).

[0032] Preferably, in step 2.1, the radial velocity value V(k) of the target is obtained according to the state estimation of the target by the radar at a certain moment, as shown in formula (4):

[0033]

[0034] In the formula, It means that at time k, radar l obtains the coordinate of target i on the x-axis through Kalman filtering. It means that at time k, radar l obtains the speed of target i on the x-axis through Kalman filtering. It means that at time k, radar l obtains the coordinate of target i on the y-axis through Kalman filtering. It means that at time k, radar l obtains the speed of target i on the y-axis through Kalman filtering. It means that at time k, radar l obtains the coordinate of target i on the z-axis through Kalman filtering. It means that at time k, radar l obtains the speed of target i on the z-axis through Kalman filtering.

[0035] Preferably, in step 2.2, the radar signal frequency domain feature statistic η2(k) is obtained by the following formula:

[0036]

[0037] In the formula, is the Doppler velocity measurement of the target by the reference radar at time k, is the variance of the radial velocity estimate, is the variance of Doppler velocity measurement error; the statistic η2(k) obeys the χ2 with 1 degree of freedom. 2 distribution, that is, χ2(k)~χ 2 (1).

[0038] Preferably, in step 3.1, the single-time spatial feature statistic ω1(k) is expressed by the following formula:

[0039]

[0040] In the formula, λ1 is a function of η1(k)~χ 2 (3) Through χ 2 The threshold value obtained by distribution.

[0041] Preferably, in step 3.2, the time-frequency domain statistics It is obtained by the following formula:

[0042]

[0043] Where N is the total number of radar observation times.

[0044] Preferably, in step 4.1, the single-time spatial feature statistic ω2(k) is expressed by the following formula:

[0045]

[0046] Among them, λ2 is composed of η2(k)~χ 2 (1) Through χ 2 The threshold value obtained by distribution;

[0047] In step 4.2, the time-frequency domain statistics It is obtained by the following formula:

[0048]

[0049] Where N is the total number of radar observation times.

[0050] Preferably, in step 5.1, the statistics corresponding to the spatiotemporal-frequency fusion features are obtained by the following formula:

[0051]

[0052] where, Ω N is the statistic corresponding to the spatio-temporal-frequency fusion feature.

[0053] Preferably, in step 5.2, the distance-velocity composite deception jamming decision rule is as follows:

[0054] If Ω N < M, it is determined that the radar is under the distance-velocity composite deception jamming;

[0055] If Ω N ≥ M, it is determined that the radar is not under the distance-velocity composite deception jamming;

[0056] where, M is the set threshold.

[0057] Compared with the prior art, the present invention has the following advantages:

[0058] The present invention overcomes the limitations of using single features such as spatial domain and frequency domain for radar jamming pattern recognition, and provides a method for identifying distance-velocity composite deception jamming based on spatio-temporal-frequency fusion features. The statistics corresponding to the spatial domain feature and frequency domain feature of the radar signal are selected, and on this basis, multiple cumulative statistics corresponding to the spatial domain and frequency domain features at multiple radar observation times and the statistic corresponding to the spatio-temporal-frequency fusion feature are respectively constructed. By selecting an appropriate threshold, the distance-velocity composite deception jamming is judged, which has good recognition performance for distance-velocity deception and is beneficial to radar jamming recognition and taking corresponding countermeasures.

[0059] The present invention utilizes the spatial domain feature that the range-velocity false target presents different spatial positions in different radar measurements, and the feature that the Doppler velocity measurement result of the range-velocity false target is difficult to match the radial velocity estimation result obtained after the radar Kalman filter tracking. Based on the spatial domain feature statistic and frequency domain feature statistic, a statistic with spatio-temporal-frequency fusion features is constructed, and according to the decision rule, it is judged whether the radar is under the distance-velocity composite deception. By adopting the method of fusing multiple features, on the basis of improving the recognition probability of deception jamming, the false judgment probability of the distance-velocity composite deception jamming is reduced. Description of the Drawings

[0060] Figure 1 is a flow chart of a method for identifying distance-velocity composite deception jamming based on spatio-temporal-frequency fusion features in the present invention;

[0061] Figure 2 is a schematic diagram of the true motion trajectories of the target and the jammer in the embodiment of the present invention;

[0062] Figure 3 is a schematic diagram of the effect of distance-velocity composite deception jamming in the embodiment of the present invention. Detailed Embodiments

[0063] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0064] like Figure 1 As shown, the technical solution of the present invention provides a distance-speed deception interference identification method based on time-space-frequency feature fusion, comprising the following steps:

[0065] Step 1: Selecting spatial feature statistics of radar signals;

[0066] Step 1.1, obtain the target's spatial position estimation coordinates and position error covariance matrix based on the radar's state estimation of the target at a certain moment;

[0067] Step 1.2, constructing the spatial domain feature statistics of the radar signal according to the target spatial position estimation coordinates and position error covariance matrix obtained in step 1.1;

[0068] Step 2: Selecting frequency domain characteristic statistics of radar signals;

[0069] Step 2.1, obtaining the radial velocity value of the target according to the radar state estimation of the target at a certain moment;

[0070] Step 2.2, constructing the frequency domain characteristic statistics of the radar signal according to the radial velocity value of the target obtained in step 2.1;

[0071] Step 3: constructing the time-space domain characteristic statistics of radar signals;

[0072] Step 3.1, construct single-time spatial domain feature statistics;

[0073] Step 3.2, constructing the time-frequency domain statistics according to the single-time spatial domain feature statistics constructed in step 3.1;

[0074] Step 4: constructing the time-frequency domain characteristic statistics of radar signals;

[0075] Step 4.1, construct single-time frequency domain feature statistics;

[0076] Step 4.2, constructing time-frequency domain statistics according to the single-time frequency domain feature statistics constructed in step 4.1;

[0077] Step 5: Determine the time-space-frequency fusion characteristics of radar signals;

[0078] Step 5.1, construct the statistics corresponding to the time-space-frequency fusion features;

[0079] Step 5.2: Based on the statistics corresponding to the time-space-frequency fusion features constructed in step 5.1, make a distance-speed composite deception interference judgment.

[0080] The present invention takes the situation that the radar network is interfered by the distance-speed compound deception as the research background, utilizes the characteristic differences between the real target and the false target in the air domain, frequency domain and time domain, extracts the air domain characteristics and frequency domain characteristics of the echo point trace at a single moment after forming the possible target track of the echo point trace, and performs the threshold detection on the statistics corresponding to the air domain characteristics and frequency domain characteristics at each moment, and the detection result is represented by "0" or "1". Finally, the detection results are accumulated at multiple moments, so as to complete the judgment on whether the radar is interfered by the distance-speed compound deception.

[0081] The following is a detailed description of a distance-speed deception interference identification method based on time-space-frequency feature fusion provided in the technical solution of the present invention through a specific example:

[0082] In this embodiment, the specific conditions are as follows:

[0083] (1) A radar network consists of two pulse radars 100 km apart. Radar 1 is a reference radar with a speed measurement function. The coordinates of Radar 1 in the rectangular coordinate system are (0, 0, 0), and the coordinates of Radar 2 in the rectangular coordinate system are (100000m, 0, 0). The scanning period of both radars is 1 s, and the total observation time of the radars on the target is 20 s.

[0084] (2) In the airspace monitored by the radar network, three fighter jets of a certain type appear to be moving in a parallel formation in a uniform straight line along a certain direction, such as Figure 2 As shown. The initial coordinates of target 1 are (30000m, 55000m, 8000m), the initial coordinates of target 2 are (34000m, 35000m, 8000m), and the initial coordinates of target 3 are (45000m, 30000m, 8000m). The flight speeds of the three targets are the same in magnitude and direction, and the speed components in the x-axis, y-axis and z-axis directions are -100m / s, -100m / s, and 0m / s respectively.

[0085] (3) In order to cover the flight of the formation and avoid tracking by the radar network, an electronic warfare aircraft of a certain type accompanied the target formation and used its own jamming equipment to simultaneously launch distance-speed composite deception jamming to the two radars. The initial coordinates of the jammer were (38000m, 45000m, 9000m), and the speed components of the jammer in the x-axis, y-axis and z-axis directions were -100m / s, -100m / s and 0m / s respectively.

[0086] (4) The radar measurement vector Z(k,i) of the i-th target at time k is:

[0087]

[0088] In the formula, ρ i (k),θ i (k), β i (k), v i (k) are the radar's measurements of radial distance, azimuth, elevation, and radial velocity of the i-th target at time k. When the radar is interfered by the range-velocity composite deception, assuming that the m-th measurement at time k is a range-velocity false target, then the measurement vector Z of the range-velocity false target at this time is J It can be expressed as the following formula:

[0089]

[0090] In the formula, ρ τ (k),θ τ (k), β τ (k), v τ (k) represents the distance, azimuth, elevation and radial velocity of the real target at time k; ρ J (k), v J (k) respectively represent the deception amount of the false target’s distance and radial velocity; ε ρ (k), ε θ (k), ε β (k), ε v (k) represents the radar measurement error, where σ ρ , σ θ , σ β , σ v Respectively represent the standard deviation of radar measurement errors for distance, azimuth, elevation angle, and speed.

[0091] In this embodiment, the standard deviation σ of the ranging errors of the two radars is ρ The standard deviation of the azimuth error is 50m. θ The standard deviation of the pitch angle error is 0.1°. β are all 0.1°, and the standard deviation of the speed measurement error of radar 1 is σ v is 1m / s.

[0092] (5) If the number of false targets generated by the jammer is 10 at a time, the jamming effect of the distance-speed composite deception jamming on the radar network is as follows: Figure 3 Assume that the jammer generates a false signal with a deceptive speed v J (k) is the deception distance ρ of 100 m / s J (k) is 600m.

[0093] (6) Since there are errors in the radar's detection of targets, the result of a single calculation has a certain one-sidedness. Therefore, multiple simulation calculations are used to count whether the radar network can effectively identify the distance-speed deception interference. The number of simulations in this embodiment is 200, and the final result is given in the form of recognition probability.

[0094] In combination with the specific conditions of the embodiment, the distance-speed interference identification method based on the fusion of time-space-frequency features includes the following steps:

[0095] Step 1: Select the spatial feature statistics of radar signals, including:

[0096] Step 1.1, obtain the state estimation vector of target i through Kalman filtering based on radar l at time k:

[0097]

[0098] The estimated spatial position coordinates of target i by the lth radar are obtained as follows:

[0099]

[0100] Calculate the position error covariance matrix of radar l to target i at time k

[0101] Step 1.2, construct the spatial feature statistics of radar signal:

[0102]

[0103] η1(k) obeys χ with 3 degrees of freedom 2 distributed.

[0104] Step 2: Select the frequency domain characteristic statistics of radar signals, including:

[0105] Step 2.1: Get the state estimation vector of target i through Kalman filtering based on radar l at time k Get the radar state estimate of the target at a certain moment and get the radial velocity value of the target

[0106]

[0107] Step 2.2, construct the frequency domain characteristic statistics of radar signal:

[0108]

[0109] Step 3: Construction of the time-space domain feature statistics of radar signals, including:

[0110] Step 3.1, construct the single-time spatial feature statistics ω1(k):

[0111]

[0112] λ1 is obtained from η1(k)~χ 2 (3) Through χ 2 The threshold value obtained by the distribution.

[0113] Step 3.2, construct the time-frequency domain statistic

[0114]

[0115] N takes 20.

[0116] Step 4: Construct the time-frequency domain feature statistic of the radar signal, specifically including:

[0117] Step 4.1, construct the single-moment time-frequency domain feature statistic ω2(k):

[0118]

[0119] λ2 is obtained from η2(k)~χ 2 (1) Through χ 2 The threshold value obtained by the distribution.

[0120] Step 4.2, construct the time-frequency domain statistic

[0121]

[0122] N takes 20.

[0123] Step 5: Fine judgment of the spatio-temporal-frequency fusion feature of the radar signal, specifically including:

[0124] Step 5.1, construct the spatio-temporal-frequency fusion feature statistic Ω N :

[0125]

[0126] Step 5.2, distance-velocity composite deception jamming judgment:

[0127] If Ω N < M, it is determined that the radar is under the distance-velocity composite deception jamming,

[0128] If Ω N ≥ M, it is determined that the radar is not under the distance-velocity composite deception jamming,

[0129] where M is the set threshold value.

[0130] Step 6: After simulating the above calculation process 200 times, it is found that under a given situation, when the deception distance of the jammer to generate false signals is 600m and the deception speed is 100m / s, the number of times the radar network can be determined to be subjected to distance-speed composite deception is 192 times, and the recognition probability is 96%.

[0131] The above are preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A distance-speed deception interference identification method based on time-space-frequency feature fusion, characterized by: The method comprises the following steps: Step 1: Selecting spatial feature statistics of radar signals; Step 1.1, obtain the target's spatial position estimation coordinates and position error covariance matrix based on the radar's state estimation of the target at a certain moment; Step 1.2, constructing radar signal spatial domain feature statistics according to the target spatial position estimation coordinates and position error covariance matrix obtained in step 1.1; Step 2: Selecting frequency domain characteristic statistics of radar signals; Step 2.1, obtaining the radial velocity value of the target according to the radar state estimation of the target at a certain moment; Step 2.2, constructing frequency domain characteristic statistics of radar signal according to the radial velocity value of the target obtained in step 2.1; Step 3: constructing the time-space domain characteristic statistics of radar signals; Step 3.1, construct single-time spatial domain feature statistics; Step 3.2, constructing time-frequency domain statistics according to the single-time spatial domain feature statistics constructed in step 3.1; Step 4: constructing the time-frequency domain characteristic statistics of radar signals; Step 4.1, construct single-time frequency domain feature statistics; Step 4.2, constructing time-frequency domain statistics according to the single-time frequency domain feature statistics constructed in step 4.1; Step 5: Determine the time-space-frequency fusion characteristics of radar signals; Step 5.1, construct the statistics corresponding to the time-space-frequency fusion features; Step 5.2: Perform distance-speed composite deception interference judgment based on the statistics corresponding to the time-space-frequency fusion features constructed in step 5.

1.

2. A distance-speed deception interference identification method based on time-space-frequency feature fusion as claimed in claim 1, characterized in that: In step 1.1, the target's spatial position estimation coordinates and position error covariance matrix are obtained by the following formula: Assume that at time k, radar l obtains the state estimation vector of target i through Kalman filtering: for: In the formula, It means that at time k, radar l obtains the coordinate of target i on the x-axis through Kalman filtering. It means that at time k, radar l obtains the speed of target i on the x-axis through Kalman filtering. It means that at time k, radar l obtains the coordinate of target i on the y-axis through Kalman filtering. It means that at time k, radar l obtains the speed of target i on the y-axis through Kalman filtering. It means that at time k, radar l obtains the coordinate of target i on the z-axis through Kalman filtering. It means that at time k, radar l obtains the speed of target i on the z-axis through Kalman filtering; The spatial position coordinates of the target are estimated by the state estimation vector of the lth radar for: In the formula, It means that at time k, radar l obtains the coordinate of target i on the x-axis through Kalman filtering. It means that at time k, radar l obtains the coordinate of target i on the y-axis through Kalman filtering. It means that at time k, radar l obtains the coordinate of target i on the z-axis through Kalman filtering; The position error covariance matrix of radar l to target i at time k is:

3. A distance-speed deception interference identification method based on time-space-frequency feature fusion as claimed in claim 2, characterized in that: In step 1.2, the spatial feature statistics of the radar signal are as follows: In the formula, is the state estimation covariance matrix of radar 1 for target i at time k; is the covariance matrix of radar 2’s state estimation of target i at time k; the eigenvalue η1(k) obeys the χ2 with 3 degrees of freedom. 2 distribution, that is, η1(k)~χ 2 (3).

4. The distance-speed deception interference identification method based on time-space-frequency feature fusion as claimed in claim 3 is characterized by: In step 2.1, the radial velocity value V(k) of the target is obtained according to the state estimation of the radar at a certain moment, as shown in formula (4): In the formula, It means that at time k, radar l obtains the coordinate of target i on the x-axis through Kalman filtering. It means that at time k, radar l obtains the speed of target i on the x-axis through Kalman filtering. It means that at time k, radar l obtains the coordinate of target i on the y-axis through Kalman filtering. It means that at time k, radar l obtains the speed of target i on the y-axis through Kalman filtering. It means that at time k, radar l obtains the coordinate of target i on the z-axis through Kalman filtering. It means that at time k, radar l obtains the speed of target i on the z-axis through Kalman filtering.

5. A distance-speed deception interference identification method based on time-space-frequency feature fusion as claimed in claim 4, characterized in that: In step 2.2, the radar signal frequency domain characteristic statistic η2(k) is obtained by the following formula: In the formula, is the Doppler velocity measurement of the target by the reference radar at time k, is the variance of the radial velocity estimate, is the variance of Doppler velocity measurement error; the statistic η2(k) obeys the χ2 with 1 degree of freedom. 2 distribution, that is, η2(k)~X 2 (1).

6. A distance-speed deception interference identification method based on time-space-frequency feature fusion as claimed in claim 5, characterized in that: In step 3.1, the single-time spatial feature statistic ω1(k) is expressed by the following formula: In the formula, λ1 is a function of η1(k)~χ 2 (3) Through χ 2 The threshold value obtained by distribution.

7. A distance-speed deception interference identification method based on time-space-frequency feature fusion as claimed in claim 6, characterized in that: In step 3.2, the time-frequency domain statistics Obtained by the following formula: Where N is the total number of radar observation times.

8. The distance-speed deception interference identification method based on time-space-frequency feature fusion as claimed in claim 7 is characterized by: In step 4.1, the single-time spatial feature statistic ω2(k) is expressed by the following formula: Among them, λ2 is composed of η2(k)~χ 2 (1) Through χ 2 The threshold value obtained by distribution; In step 4.2, the time-frequency domain statistics Obtained by the following formula: Where N is the total number of radar observation times.

9. A distance-speed deception interference identification method based on time-space-frequency feature fusion as claimed in claim 8, characterized in that: In step 5.1, the statistics corresponding to the spatiotemporal frequency fusion features are obtained by the following formula: In the formula, Ω N is the statistic corresponding to the spatiotemporal-frequency fusion feature.

10. The distance-speed deception interference identification method based on time-space-frequency feature fusion as claimed in claim 9, characterized in that: In step 5.2, the distance-speed composite deception interference decision rule is as follows: If Ω N <M, it is determined that the radar is under range-velocity composite deception jamming; If Ω N ≥M, it is determined that the radar is not interfered by the distance-speed composite deception; Wherein, M is the set threshold value.

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