Method and equipment for identifying radar distance velocity deception jamming

Abstract

The invention discloses a radar distance and speed deception jamming recognition method and device, and relates to the field of radar network anti-jamming, and the method comprises the steps: determining that point traces are derived from different types of deception jamming through the sudden change of the number of the point traces in the distance dimension and the speed dimension; establishing a spatial recognition feature set; based on the spatial identification feature set and a preset spatial judgment threshold, determining optimized distance deception jamming and first uncertain jamming; establishing a motion recognition feature set; determining optimized speed deception jamming and second uncertain jamming based on the motion recognition feature set and a preset motion judgment threshold; establishing a spatial motion recognition feature set; based on the spatial motion identification feature set and a preset spatial motion judgment threshold, determining optimized distance velocity deception jamming and third uncertain jamming; and performing comprehensive judgment on the first uncertain interference, the second uncertain interference and the third uncertain interference, and determining target distance speed deception interference. According to the invention, the identification rate of distance speed deception jamming is improved.

Description

Technical Field

[0001] The present application relates to the technical field of radar network anti-jamming, and particularly to a method and device for identifying radar range-velocity deception jamming. Background Art

[0002] With the service of new military aircraft, current active jamming devices are further developed in the directions of increasing jamming power, adding jamming modes, increasing the number of jammings, and using multiple jammings simultaneously, which brings severe challenges to the existing radar defense systems. Range-velocity compound deception jamming (i.e., range-velocity deception jamming) is a typical compound jamming style. It not only has a time sequence and spatial overlap, but also can be organically complementary in the airspace, time domain, and frequency domain. Its jamming style is more confusing, concealed, and has a stronger jamming effect.

[0003] In the research on the identification and suppression of range-velocity deception jamming, most of the existing research is based on chi-square test decision. For a single radar, the method of radial velocity estimation and velocity measurement comparison can be used for identification; for a radar network, the spatial feature differences of different radars can be used for identification. However, most of these methods are for identifying the range deception jamming characteristics of unknown electronic jamming, and less research has been involved in the impact of velocity deception on target detection and tracking and jamming identification. In response to this situation, the methods of dual-channel tracking comparison and radial velocity comparison can effectively identify velocity deception jamming, but these methods cannot simultaneously meet the requirements of radar and radar network for anti-range-velocity compound jamming.

[0004] To achieve effective identification of range-velocity deception jamming, a single feature is no longer sufficient to cope with this increasingly complex electromagnetic jamming challenge, and the method of comprehensive extraction of multiple features has been effectively proposed. The method of comprehensive extraction of multiple features can solve the problem of identifying range-velocity deception jamming to a certain extent, but there are problems such as poor identification rate of range-velocity deception jamming caused by unreasonable feature selection and incorrect feature use order, which further leads to the problem that the identification effect of multiple features is worse than that of a single feature. Summary of the Invention

[0005] The purpose of the present application is to provide a method and device for identifying radar range-velocity deception jamming, which can solve the problem of inaccurate identification of range-velocity deception jamming in related technologies.

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

[0007] In a first aspect, the present application provides a method for identifying radar range-velocity deception jamming, including: determining that the traces originate from different types of deception jamming by using the mutations of the number of traces in the range dimension and the velocity dimension; the different types of deception jamming include range deception jamming, velocity deception jamming, and range-velocity composite deception jamming; the traces are discrete representations of the radar receiving target echoes; each trace contains the spatial position and velocity of the target; the spatial position includes range and angle. In the range dimension, for the range deception jamming, sort the different spatial error features at each moment in ascending order to establish a spatial recognition feature set. Based on the spatial recognition feature set and a preset spatial decision threshold, determine the optimized range deception jamming and the first uncertain jamming. In the velocity dimension, for the velocity deception jamming, sort the different Doppler error features at each moment in the aforesaid order to establish a motion recognition feature set. Based on the motion recognition feature set and a preset motion decision threshold, determine the optimized velocity deception jamming and the second uncertain jamming. In the range-velocity dimension, for the range-velocity deception jamming, sort the different state estimation error features at each moment in the aforesaid order to establish a spatial motion recognition feature set. Based on the spatial motion recognition feature set and a preset spatial motion decision threshold, determine the optimized range-velocity deception jamming and the third uncertain jamming. Perform a comprehensive decision on the first uncertain jamming, the second uncertain jamming, and the third uncertain jamming to determine the target range-velocity deception jamming. Take both the optimized range-velocity deception jamming and the target range-velocity deception jamming as the finally identified range-velocity deception jamming.

[0008] In a second aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the method for identifying radar range-velocity deception jamming described above.

[0009] According to the specific embodiments provided by the present application, the following technical effects are disclosed in the present application: The present application provides a method and device for identifying radar range-velocity deception jamming. First, by utilizing the mutations of the number of traces in the range dimension and the velocity dimension, the identification of the types of deception jamming in the traces is determined. This enables the present application to first conduct an existence test for different types of deception jamming when the type of deception jamming is unknown, and then perform subsequent targeted identification and suppression, which is more in line with the actual situation. Then, first, different spatial error characteristics at each moment are arranged in ascending order, and a spatial recognition feature set with hierarchical features is established. The optimized recognition of range deception jamming is completed through the spatial recognition feature set. Further, different Doppler error characteristics at each moment are arranged in ascending order, and a motion recognition feature set with hierarchical features is established. The optimized recognition of velocity deception jamming is completed through the motion recognition feature set. Finally, different state estimation error characteristics at each moment are arranged in ascending order, and a spatial-motion recognition feature set with hierarchical features is established. The optimized recognition of range-velocity composite deception jamming is completed through the spatial-motion recognition feature set. Comprehensive judgment is performed on the first uncertain jamming, the second uncertain jamming, and the third uncertain jamming identified in the range dimension, the velocity dimension, and the range-velocity dimension to determine the target range-velocity deception jamming. Both the optimized range-velocity deception jamming and the target range-velocity deception jamming are used as the finally identified range-velocity deception jamming. By identifying different features step by step in this way, the identified range-velocity deception jamming is more reasonable and accurate. It solves the problem of poor recognition rate of deception jamming types caused by the unreasonable selection of different features and the incorrect order of using multiple features. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0011] Figure 1 is a schematic flowchart of a method for identifying radar range-velocity deception jamming.

[0012] Figure 2 is a flowchart of a method for identifying range-velocity deception jamming based on hierarchical feature extraction.

[0013] Figure 3 is a diagram of a scheme for detecting the existence of range deception jamming.

[0014] Figure 4 is a diagram of a scheme for detecting the existence of velocity deception jamming.

[0015] Figure 5 is a diagram of a scheme for detecting the existence of range-velocity composite deception jamming.

[0016] Figure 6 It is a two-dimensional spatial error feature extraction diagram.

[0017] Figure 7 It is a step-by-step spatial error feature extraction and screening scheme diagram.

[0018] Figure 8 It is a two-dimensional Doppler error feature extraction diagram.

[0019] Figure 9 It is a step-by-step Doppler error feature extraction and screening scheme diagram.

[0020] Figure 10 It is a step-by-step state estimation error feature extraction and screening scheme diagram.

[0021] Figure 11 It is a comprehensive decision-making for range-velocity composite deception jamming based on multi-moment features. Specific implementation manners

[0022] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0023] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0024] As Figure 1 and Figure 2 shown, the present application provides a method for identifying radar range-velocity deception jamming, including: Step 101: Utilize the mutations of the number of traces in the range dimension and the velocity dimension to determine that the traces originate from different types of deception jamming; the different types of deception jamming include range deception jamming, velocity deception jamming, and range-velocity composite deception jamming; the trace is a discretized representation of the radar receiving the target echo; each trace contains the spatial position and velocity of the target; the spatial position includes range and angle.

[0025] Step 102: In the range dimension, for the range deception jamming, sort the different spatial error features at each moment in order, and establish a spatial recognition feature set; the order is from small to large.

[0026] Step 103: Based on the spatial recognition feature set and a preset spatial decision threshold, determine the optimized range deception jamming and the first uncertain jamming.

[0027] Step 104: In the velocity dimension, for the velocity deception interference, sort different Doppler error characteristics at each moment in the said order to establish a motion recognition feature set.

[0028] Step 105: Based on the motion recognition feature set and a preset motion decision threshold, determine the optimized velocity deception interference and the second uncertainty interference.

[0029] Step 106: In the range-velocity dimension, for the range-velocity deception interference, sort different state estimation error characteristics at each moment in the said order to establish a spatial motion recognition feature set.

[0030] Step 107: Based on the spatial motion recognition feature set and a preset spatial motion decision threshold, determine the optimized range-velocity deception interference and the third uncertainty interference.

[0031] Step 108: Perform a comprehensive decision on the first uncertainty interference, the second uncertainty interference, and the third uncertainty interference to determine the target range-velocity deception interference.

[0032] Step 109: Take both the optimized range-velocity deception interference and the target range-velocity deception interference as the finally identified range-velocity deception interference.

[0033] Specifically, the process of identifying range-velocity deception interference in this application specifically includes the following content.

[0034] 1. Utilize the characteristics of whether the number of traces in the range dimension and the Doppler dimension mutates to judge the existence of range deception interference, velocity deception interference, and range-velocity composite deception interference, and on this basis, divide the radar echo traces into four combinations: target, range deception interference, velocity deception interference, and range-velocity composite deception interference, so as to achieve refined recognition of multiple types of deception interference.

[0035] 2. In the range dimension, establish a hierarchical recognition feature set in the order of the magnitude of each feature space error. By comparing the spatial error statistics, select reasonable range deception recognition features and eliminate redundant unreasonable features, thereby completing the optimized recognition of range deception interference.

[0036] 3. In the velocity dimension, establish a hierarchical recognition feature set in the order of the magnitude of each feature Doppler error. By comparing the Doppler error statistics, select reasonable velocity deception recognition features and eliminate redundant velocity deception features, thereby completing the optimized recognition of velocity deception interference.

[0037] 4. In the range-velocity dimension, establish a hierarchical recognition feature set in the order of the magnitude of the estimation error of each feature state. By comparing the statistical quantities of the state estimation error, select reasonable range-velocity deception recognition features, eliminate redundant and unreasonable features, and complete the optimized recognition of range-velocity deception interference.

[0038] 5. On the basis of the hierarchical recognition of range deception interference, velocity deception interference, and range-velocity composite deception interference, further use multi-moment features to comprehensively identify multiple types of deception interference. Identify and suppress the determined deception interference signals, and retain the uncertain target echo signals. It is necessary to further comprehensively judge to determine whether it is target range-velocity deception interference.

[0039] Among them, the uncertain target echo signals are the first uncertain interference, the second uncertain interference, and the third uncertain interference.

[0040] In some embodiments, step 101 specifically includes: within multiple radar scan cycles before the current moment, determine the average number of traces at each moment corresponding to the range dimension, the average number of traces at each moment corresponding to the velocity dimension, and the average number of traces at each moment corresponding to the range-velocity dimension; according to the number of traces corresponding to the range dimension in the current radar scan cycle and the average number of traces at each moment corresponding to the range dimension, determine range deception interference; according to the number of traces corresponding to the velocity dimension in the current radar scan cycle and the average number of traces at each moment corresponding to the velocity dimension, determine velocity deception interference; according to the number of traces corresponding to the range-velocity dimension in the current radar scan cycle and the average number of traces at each moment corresponding to the range-velocity dimension, determine range deception interference.

[0041] In practical applications, the specific process of step 101 is as follows.

[0042] 1. Detection of the existence of deceptive interference based on the sudden change of the number of measurement points.

[0043] In the detection link of the existence of deceptive interference, use the characteristics of whether the number of traces in the range dimension and the Doppler dimension changes suddenly to judge the existence of range deception interference, velocity deception interference, and range-velocity composite deception interference, and on this basis, divide the radar echo traces into four combinations: target, range deception interference, velocity deception interference, and range-velocity composite deception interference, so as to achieve refined recognition of multiple types of deception interference.

[0044] 1) Detection of the existence of interference in the range-time dimension.

[0045] For the characteristic that interference appears after the radar has been turned on for a period of time, the existence of range deception interference is detected by using the characteristic of sudden change of measured point traces in the range-time dimension. Considering that the range-time dimension can not only count the number of point traces in the current radar scanning period, but also count the stable value of the number of point traces in multiple scanning periods, therefore, the statistical value of the number of point traces in the current radar scanning period is compared with the stable value of the number of point traces in multiple radar scanning periods to judge the existence of range deception interference, as Figure 3 shown.

[0046] Suppose at time the number of point traces in the range-time dimension within the radar scanning period is , then the average value of the number of point traces within scanning periods is:[[]]END]]

[0047] When the number of point traces within the radar scanning period at time is

[0048] , the existence problem of range deception interference can be further analyzed and judged by the following hypothesis test. If

[0049] , it is judged that there is no range deception interference within the current radar scanning period. If

[0050] , it is judged that there is range deception interference within the current radar scanning period. where

[0051] is the decision coefficient.

[0052] 2) Detection of the existence of interference in the Doppler-time dimension (i.e., the velocity dimension). Figure 4 shown.

[0053] Suppose at time the number of point traces in the Doppler-time dimension within the radar scanning period is , then the average value of the number of point traces within scanning periods is:[[]]END]]

[0054] When the number of point traces within the radar scanning period at time When the existence problem of velocity deception jamming is to be further analyzed and judged by the following hypothesis test.

[0055] If , it is judged that there is no velocity deception jamming in the current radar scanning period.

[0056] If , it is judged that there is velocity deception jamming in the current radar scanning period.

[0057] 3) Detection of the existence of jamming in the range-velocity two-dimensional plane.

[0058] The existence of range-velocity composite deceptive jamming is detected by using the characteristic of the mutation of measurement points in the range-velocity two-dimensional plane. The statistical value of the number of range-velocity two-dimensional points in the current radar scanning period is compared with the stable value of the number of range-velocity two-dimensional points in multiple radar scanning periods to judge the existence of range-velocity composite deceptive jamming, as Figure 5 shown.

[0059] Suppose at time the number of range-velocity two-dimensional points in the radar scanning period is , then the average value of the number of range-velocity two-dimensional points in scanning periods is:

[0060] When the number of range-velocity two-dimensional points in the radar scanning period at time is , the existence problem of range-velocity composite deceptive jamming is to be further analyzed and judged by the following hypothesis test.

[0061] If , it is judged that there is no range-velocity deception jamming in the current radar scanning period.

[0062] If , it is judged that there is range-velocity composite deceptive jamming in the current radar scanning period.

[0063] In some embodiments, step 102 specifically includes: based on bistatic radar networking and the Cartesian coordinate system, determining the first position information of the target detected by radar A at the current moment and the second position information of the target detected by radar B at the current moment; determining the two-dimensional spatial error characteristics according to the two-dimensional relative distance between radar A and radar B, the first position information, and the second position information; determining the three-dimensional spatial error characteristics according to the three-dimensional relative distance between radar A and radar B, the first position information, the second position information, the pitch information corresponding to radar A, and the pitch information corresponding to radar B; determining the four-dimensional spatial error characteristics according to the four-dimensional relative distance between radar A and radar B, the first position information, the second position information, the pitch information corresponding to radar A, the pitch information corresponding to radar B, the radial velocity corresponding to radar A, and the radial velocity corresponding to radar B; the different spatial error characteristics include two-dimensional spatial error characteristics, three-dimensional spatial error characteristics, and four-dimensional spatial error characteristics; sorting the different spatial error characteristics from smallest to largest, and using the sorted spatial error characteristic sequence as the first recognition feature set at the current moment; using the first recognition feature set at each moment as the spatial recognition feature set.

[0064] In some embodiments, step 104 specifically includes: determining the estimated value of the two-dimensional radial velocity at moment K according to the two-dimensional radial distances of the radar at moment K - 1 and moment K, and determining the two-dimensional Doppler error characteristic according to the estimated value of the two-dimensional radial velocity and the measured value of the two-dimensional radial velocity; determining the estimated value of the three-dimensional radial velocity at moment K according to the three-dimensional radial distances of the radar at moment K - 1 and moment K, and determining the three-dimensional Doppler error characteristic according to the estimated value of the three-dimensional radial velocity and the measured value of the three-dimensional radial velocity; using target tracking technology to determine the target state vector at moment K, and using the target state vector at moment K to determine the estimated value of the four-dimensional radial velocity at moment K; determining the four-dimensional Doppler error characteristic according to the estimated value of the four-dimensional radial velocity at moment K and the measured value of the four-dimensional radial velocity at moment K; the different Doppler error characteristics include two-dimensional Doppler error characteristics, three-dimensional Doppler error characteristics, and four-dimensional Doppler error characteristics; sorting the different Doppler error characteristics from smallest to largest, and using the sorted Doppler error characteristics as the second recognition feature set at moment K; using the second recognition feature set at each moment as the motion recognition feature set.

[0065] In some embodiments, step 106 specifically includes: based on bistatic radar networking and target tracking technology, using the two-dimensional measurements of the radar, respectively determining the two-dimensional state estimates of radar A and radar B at a moment; according to the two-dimensional state estimates of radar A and radar B, and the two-dimensional state estimation error matrices of radar A and radar B, determining the two-dimensional state estimation error characteristics; using the three-dimensional measurements of the radar, respectively determining the three-dimensional state estimates of radar A and radar B; according to the three-dimensional state estimates of radar A and radar B, and the three-dimensional state estimation error matrices of radar A and radar B, determining the three-dimensional state estimation error characteristics; using the four-dimensional measurements of the radar, respectively determining the four-dimensional state estimates of radar A and radar B; according to the four-dimensional state estimates of radar A and radar B, and the four-dimensional state estimation error matrices of radar A and radar B, determining the four-dimensional state estimation error characteristics; the different state estimation error characteristics include two-dimensional state estimation error characteristics, three-dimensional state estimation error characteristics, and four-dimensional state estimation error characteristics; sorting the different state estimation error characteristics at the current moment from smallest to largest, and taking the sorted state estimation error characteristics as the third recognition feature set at the current moment; taking the third recognition feature sets at each moment as the spatial motion recognition feature set.

[0066] In some embodiments, step 103 specifically includes: for different spatial error characteristics at any moment, the different spatial error characteristics include two-dimensional spatial error characteristics, three-dimensional spatial error characteristics, and four-dimensional spatial error characteristics; when the first condition is satisfied, determining that the trace originates from a real target; the first condition is that the different spatial error characteristics are all less than a preset spatial decision threshold; when the second condition is satisfied, determining that the trace originates from an optimized range deception interference; the second condition is that the different spatial error characteristics are all greater than or equal to the preset spatial decision threshold; when neither the first condition nor the second condition is satisfied, determining it as a first uncertain interference.

[0067] In some embodiments, step 105 specifically includes: for different Doppler error characteristics at any moment, the different Doppler error characteristics include two-dimensional Doppler error characteristics, three-dimensional Doppler error characteristics, and four-dimensional Doppler error characteristics; when the different Doppler error characteristics at the moment satisfy the third condition, determining that the trace originates from a real target; the third condition is that the different Doppler error characteristics are all less than a preset motion decision threshold; when the different Doppler error characteristics at the moment satisfy the fourth condition, determining that the trace originates from an optimized velocity deception interference; the fourth condition is that the different Doppler error characteristics are all greater than or equal to the preset motion decision threshold; when the different Doppler error characteristics at the moment do not satisfy both the third condition and the fourth condition, determining it as a second uncertain interference.

[0068] In some embodiments, step 107 specifically includes: for the different state estimation error characteristics at any moment, the different state estimation error characteristics including two-dimensional state estimation error characteristics, three-dimensional state estimation error characteristics, and four-dimensional state estimation error characteristics; when the fifth condition is satisfied, it is determined that the trace originates from a real target; the fifth condition is that the different state estimation error characteristics are all less than a preset spatial motion decision threshold; when the sixth condition is satisfied, it is determined that the trace originates from an optimized range-velocity deception interference; the sixth condition is that the different state estimation error characteristics are all greater than or equal to the preset spatial motion decision threshold; when neither the fifth condition nor the sixth condition is satisfied, it is determined as a third uncertain interference.

[0069] In some embodiments, step 108 specifically includes: determining a comprehensive statistical decision quantity according to the first uncertain interference, the second uncertain interference, the third uncertain interference, and according to a preset spatial decision threshold, a preset motion decision threshold, and a preset spatial motion decision threshold; determining whether the target echo is the target range-velocity deception interference according to a preset comprehensive decision threshold and the comprehensive statistical decision quantity.

[0070] In practical applications, the specific processes of steps 102-109 are as follows.

[0071] 1. Hierarchical range deception interference recognition based on the magnitude of spatial error.

[0072] 1) Two-dimensional spatial error feature extraction.

[0073] Based on the bistatic radar network, assume At time, the measurement of radar A is , and the measurement of radar B is , then their measurements in the Cartesian coordinate system can be respectively expressed as: .

[0074] Among them, is the first position information of the target detected by radar A at time; is the second position information of the target detected by radar B at time; is the two-dimensional radial distance of the target detected by radar B, is the angle of the target detected by radar B; is the two-dimensional radial distance of the target detected by radar B, is the angle of the target detected by radar B, is the transpose.

[0075] Correspondingly, the measurement error matrices corresponding to radar A and radar B in the Cartesian coordinate system are and , which can be obtained using measurement errors and will not be elaborated here. is the two-dimensional relative distance between radars A and B, as Figure 6 shown.

[0076] By utilizing the spatial feature differences between the range deception and the real target in the dual radars, two-dimensional spatial error features can be extracted .

[0077] .

[0078] 2) Extraction of three-dimensional spatial error features.

[0079] On the basis of the extraction of two-dimensional spatial error features, the pitch information is further introduced, as Figure 7 shown, and its measurement in the three-dimensional rectangular coordinate system can be expressed as: .

[0080] Among them, is the first position information of the target detected by radar A in the three-dimensional space at ; is the second position information of the target detected by radar B in the three-dimensional space at ; and are the pitch information measured by radars A and B respectively at .

[0081] By utilizing the three-dimensional spatial feature differences between the range deception and the real target in the dual radars, three-dimensional spatial error features can be extracted : .

[0082] Among them, and are respectively and 's three-dimensional measurement error matrices, is the three-dimensional relative distance between radars A and B.

[0083] 3) Extraction of four-dimensional spatial error features.

[0084] On the basis of the extraction of three-dimensional spatial error features, the radial information is further introduced, as Figure 7 shown, and its measurement in the four-dimensional rectangular coordinate system can be expressed as follows.

[0085] .

[0086] Among them, is the first position information of the target detected by radar A in the four-dimensional space at The first position information of the target detected at the moment; Is the second position information of the target detected by the four-dimensional space radar B at the moment.

[0087] By utilizing the difference in the four-dimensional space characteristics of the distance deception and the real target in the dual radars, the four-dimensional space error characteristics can be extracted : .

[0088] Among them, and are the radial velocity measurements of radar A and radar B at the moment respectively, and are respectively and 's four-dimensional measurement error matrices, is the four-dimensional relative distance between radar A and B.

[0089] 4) Sorting and selection of spatial error characteristics.

[0090] Sort different spatial error characteristics , and (with the introduction of more measurement information, it is not limited to these three characteristic quantities here) in ascending order, and eliminate the characteristic quantities with relatively large errors to solve the misjudgment or missed judgment problems caused by characteristic redundancy or unreasonable characteristic selection, as Figure 7 shown.

[0091] 5) Optimization recognition of distance deception interference.

[0092] For the screened spatial error characteristics, establish a multi-level decision mechanism according to the number of uses. When selecting the minimum characteristic for decision-making, it is a first-level decision; when selecting the minimum two characteristics for decision-making, it is a second-level decision; when selecting the minimum three characteristics for decision-making, it is a third-level decision; and so on.

[0093] a) First-level decision.

[0094] On the above basis, the problem of distance deception interference recognition and suppression can be optimally judged by the following hypothesis test (assuming is the minimum value).

[0095] When , it is judged as the real distance.

[0096] When , it is judged as distance deception and eliminated.

[0097] Among them, is the first-level decision statistic after screening, is the first-level decision threshold, which can be obtained by using statistical knowledge for testing.

[0098] b) Second-level decision.

[0099] On the above basis, the problem of distance deception interference recognition and suppression can be optimized by the following hypothesis test (assuming and are the two smallest values) as follows.

[0100] When and , it is judged as the true distance.

[0101] When and , it is judged as distance deception and should be excluded.

[0102] In other cases, it is uncertain interference and is sent for comprehensive decision-making at multiple times.

[0103] Among them, and are the second-level decision statistics after screening, and are the second-level decision thresholds, which can be obtained by using statistical knowledge for testing.

[0104] c) Third-level decision.

[0105] On the above basis, the problem of distance deception interference recognition and suppression can be optimized by the following hypothesis test as follows.

[0106] When and and , it is judged as the true distance.

[0107] When and and , it is judged as distance deception and should be excluded.

[0108] In other cases, it is uncertain interference and is sent for comprehensive decision-making at multiple times.

[0109] Among them, , and are the third-level decision statistics after screening, , and are the third-level decision thresholds, which can be obtained by using statistical knowledge for testing.

[0110] 3. Recognition of progressive range deception jamming based on the magnitude of Doppler error.

[0111] 1) Extraction of two-dimensional Doppler error characteristics.

[0112] Assume time and the two-dimensional radial distances of the radar at time are respectively and , then the radial velocity estimation at time can be expressed as: .

[0113] By using the difference in two-dimensional Doppler characteristics between velocity deception and real targets, the two-dimensional Doppler error characteristics can be extracted, as Figure 8 shown.

[0114] .

[0115] Among them, is the measured two-dimensional radial velocity, T is the sampling interval, and are respectively the radial velocity estimation error and the radial velocity measurement error.

[0116] 2) Extraction of three-dimensional Doppler error characteristics.

[0117] Assume time and the three-dimensional radial distances of the radar at time are respectively and , then the radial velocity estimation at time can be expressed as: .

[0118] By using the difference in three-dimensional Doppler characteristics between velocity deception and real targets, as Figure 9 shown, the three-dimensional Doppler error characteristics can be extracted as follows.

[0119] .

[0120] Among them, is the measured three-dimensional radial velocity, T is the sampling interval, and are respectively the radial velocity estimation error and the radial velocity measurement error.

[0121] 3) Extraction of four-dimensional Doppler error characteristics.

[0122] Assume The target state vector obtained by using target tracking technology at all times is: .

[0123] Among them, 、 、 and are respectively the direction position estimation, direction velocity estimation, direction position estimation, direction velocity estimation of target tracking.

[0124] Then its radial velocity estimation at time can be expressed as: .

[0125] Using the differences in the four-dimensional multi-Doppler characteristics between velocity deception and real targets, as Figure 9 shown, the four-dimensional Doppler error characteristics can be extracted: .

[0126] Among them, is the measured radial velocity, with the same value as in the two-dimensional case; and are respectively the radial velocity estimation error and the radial velocity measurement error.

[0127] 4) Sorting and selection of Doppler error characteristics.

[0128] Sort the different velocity error characteristics 、 and (with the introduction of more measurement information, it is not limited to these three characteristic quantities here) in ascending order, and eliminate the characteristic quantities with large errors to solve the misjudgment or missed judgment problems caused by characteristic redundancy or unreasonable characteristic selection, as Figure 9 shown.

[0129] 5) Optimized identification of velocity deception interference.

[0130] For the selected Doppler error characteristics, establish a multi-level decision-making mechanism according to the number of uses. When selecting the smallest characteristic for decision-making, it is the first-level decision; when selecting the smallest two characteristics for decision-making, it is the second-level decision; when selecting the smallest three characteristics for decision-making, it is the third-level decision; and so on. Among them, the uncertain interference in this part is the second uncertain interference.

[0131] a) First-level decision.

[0132] Based on the above, the problem of range deception interference identification and suppression can be optimized and judged by the following hypothesis test (assuming is the minimum value).

[0133] When it is judged as the true speed.

[0134] When it is judged as speed deception and eliminated.

[0135] Among them, is the first-level judgment statistic after screening, is the first-level judgment threshold, which can be obtained by using statistical knowledge for testing.

[0136] b) Second-level judgment.

[0137] Based on the above, the problem of range deception interference identification and suppression can be optimized and judged by the following hypothesis test (assuming and are the two minimum values).

[0138] When and it is judged as the true speed.

[0139] When and it is judged as speed deception and eliminated.

[0140] In other cases, it is uncertain interference and sent for comprehensive judgment at multiple moments.

[0141] Among them, and are the second-level judgment statistics after screening, and are the second-level judgment thresholds, which can be obtained by using statistical knowledge for testing.

[0142] c) Third-level judgment.

[0143] Based on the above, the problem of range deception interference identification and suppression can be optimized and judged by the following hypothesis test.

[0144] When and and it is judged as the true speed.

[0145] When and and it is judged as speed deception and eliminated.

[0146] In other cases, it is an uncertain interference and is sent for comprehensive decision-making at multiple moments.

[0147] Among them, 、 and are the three-level decision-making statistics after screening, 、 and are the three-level decision-making thresholds, which can be obtained by using to test statistical knowledge.

[0148] 4. Hierarchical distance-velocity composite deception interference recognition based on the magnitude of state estimation error.

[0149] 1) Two-dimensional state estimation error feature extraction.

[0150] Based on the bistatic radar network, through existing target tracking technologies, the state estimation can be obtained using the two-dimensional measurements of radar A, and the state estimation can be obtained using the two-dimensional measurements of radar B. At this time, by utilizing the differences in the state estimation error features of range-velocity deception and real targets in the two radars, as shown in Figure 10 , the two-dimensional state estimation error feature can be extracted: .

[0151] Among them, and are the state estimation error matrices of and respectively, which can be obtained during the target tracking process.

[0152] 2) Three-dimensional state estimation error feature extraction.

[0153] Based on the extraction of two-dimensional state estimation error features, further introduce the pitch information. Through existing target tracking technologies, the state estimation can be obtained using the three-dimensional measurements of radar A, and the state estimation can be obtained using the three-dimensional measurements of radar B. At this time, by utilizing the differences in the state estimation error features of range-velocity composite deception and real targets in the two radars, as shown in Figure 10 , the three-dimensional state estimation error feature can be extracted.

[0154] .

[0155] Among them, and are the state estimation error matrices of and The state estimation error matrix can be obtained during the target tracking process.

[0156] 3) Four-dimensional state estimation error feature extraction.

[0157] Based on the four-dimensional state estimation error feature extraction, the radial information is further introduced. Through the wide-dimensional target tracking technology, the state estimation can be obtained using the four-dimensional measurements of Radar A , and the state estimation can be obtained using the four-dimensional measurements of Radar B . At this time, by using the differences in the state estimation error features of the range-velocity composite deception and the real target in the two radars, as Figure 10 shown, the four-dimensional state estimation error features can be extracted : .

[0158] Among them, and are respectively and 's state estimation error matrices, which can be obtained during the target tracking process.

[0159] 4) Sorting and selection of different state estimation error features.

[0160] Sort the different state estimation error features , and (with the introduction of more measurement information, it is not limited to these three feature quantities here) in ascending order, and eliminate the feature quantities with relatively large errors to solve the misjudgment or missed judgment problems caused by feature redundancy or unreasonable feature selection, as Figure 10 shown.

[0161] 5) Optimization and identification of range-velocity interference.

[0162] For the selected different state estimation error features, a multi-level decision-making mechanism is established according to the number of uses. When the minimum feature is selected for decision-making, it is the first-level decision; when the two smallest features are selected for decision-making, it is the second-level decision; when the three smallest features are selected for decision-making, it is the third-level decision; and so on. Among them, the uncertain interference in this part is the third uncertain interference.

[0163] a) First-level decision.

[0164] On the above basis, the problem of range deception interference identification and suppression can be optimally judged by the following hypothesis test (assuming is the minimum value).

[0165] When , it is judged as a real target.

[0166] When When it is, the decision is spoofing interference and it is eliminated.

[0167] Among them, is the first-level decision statistic after screening, is the first-level decision threshold, which can be obtained by using statistical knowledge of hypothesis testing.

[0168] b) Second-level decision.

[0169] On this basis, the problem of range spoofing interference identification and suppression can be optimized by the following hypothesis testing (assuming and are the two smallest values).

[0170] When and it is, the decision is a real target.

[0171] When and it is, the decision is spoofing interference and it is eliminated.

[0172] When it is other cases, it is uncertain interference and is sent for comprehensive decision at multiple times.

[0173] Among them, and are the second-level decision statistics after screening, and are the second-level decision thresholds, which can be obtained by using statistical knowledge of hypothesis testing.

[0174] c) Third-level decision.

[0175] On this basis, the problem of range spoofing interference identification and suppression can be optimized by the following hypothesis testing.

[0176] When and and it is, the decision is the true range.

[0177] When and and it is, the decision is range spoofing and it is eliminated.

[0178] When it is other cases, it is uncertain interference and is sent for comprehensive decision at multiple times.

[0179] Among them, , and are the third-level decision statistics after screening, , and is the third-level decision threshold, which can be obtained by using statistical knowledge for verification.

[0180] 5. Comprehensive decision on range-velocity deception jamming based on multi-moment features.

[0181] To effectively improve the correct recognition probability of range-velocity combined deception jamming, based on the decision at a single moment, the feature differences at multiple moments are used to further identify range-velocity combined deception jamming, as Figure 11 shown.

[0182] The recognition results of range-velocity deception jamming at different moments are input into the comprehensive decision system, and then the range-velocity combined deception jamming is centrally analyzed and discriminated within the comprehensive decision system using the scoring method (taking the first-level decision situation as an example) as follows.

[0183] If and and , then .

[0184] If and and , then .

[0185] If and and , then .

[0186] If and and , then .

[0187] If and and then .

[0188] If and and then .

[0189] If and and then .

[0190] If and and then .

[0191] Based on the preliminary judgment of range-velocity deception jamming using different spatial error characteristics, different Doppler error characteristics, and different state estimation error characteristics, let .

[0192] Among them, is the comprehensive statistical decision-making quantity at multiple moments, is the comprehensive statistical decision-making quantity at the moment.

[0193] The problem of range-velocity composite jamming recognition based on multiple moments can be further analyzed and judged by the following hypothesis test.

[0194] If , then the target echo originates from range-velocity deception jamming.

[0195] If , then the target echo originates from a real target.

[0196] Among them, is the comprehensive decision threshold, which is determined by the observation duration.

[0197] Specifically, the purpose of this application is to break through the constraints of traditional radar anti-jamming methods, solve the problems of target, range deception jamming, velocity deception jamming, and range-velocity deception jamming recognition, and then improve the ability of target detection and tracking under the combined influence of range deception and velocity deception. It can solve the following problems.

[0198] 1) The problem of the existence test of multiple types of range deception and velocity deception jamming under the condition of unknown jamming patterns.

[0199] 2) The problem of low positive recognition probability caused by unreasonable selection of recognition features in the existing range deception jamming recognition technology, velocity deception jamming recognition technology, and range-velocity composite deception jamming recognition technology.

[0200] 3) In multi-feature recognition, the problem that the multi-feature recognition effect is worse than that of single-feature due to feature redundancy.

[0201] 4) In multi-feature recognition, the problem that the multi-feature recognition effect is worse than that of single-feature due to inappropriate order of feature use.

[0202] Compared with the related technology, the new method for range-velocity composite deception jamming recognition based on hierarchical feature extraction described in this application has the following beneficial effects: 1) Most of the related technologies are based on the assumption of known jamming patterns, and identify and suppress the known jamming patterns accordingly; while this application is based on the condition of unknown jamming patterns, first conducts the existence test of different jamming patterns, and then conducts targeted identification and suppression, which is more in line with the actual situation.

[0203] 2) This application is an improvement on the existing method for identifying range deception jamming. This method establishes a hierarchical recognition feature set in the order of the magnitude of the error in each feature space. By comparing the statistical quantities of the spatial errors, reasonable recognition features can be selected, redundant and unreasonable features can be eliminated, and then the optimized recognition of range deception jamming can be completed.

[0204] 3) This application is an improvement on the existing method for identifying velocity deception jamming. This method establishes a hierarchical recognition feature set in the order of the magnitude of the Doppler error of each feature. By comparing the statistical quantities of the Doppler errors, reasonable recognition features can be selected, redundant and unreasonable features can be eliminated, and then the optimized recognition of velocity deception jamming can be completed.

[0205] 4) This application is an improvement on the existing method for identifying combined range-velocity deception jamming. This method establishes a hierarchical recognition feature set in the order of the magnitude of the state estimation error of each feature. By comparing the statistical quantities of the state estimation errors, reasonable recognition features can be selected, redundant and unreasonable features can be eliminated, and then the optimized recognition of combined range-velocity deception jamming can be completed.

[0206] 5) The hierarchical feature recognition technology proposed in this application can solve the problem that the recognition effect of multiple features is inferior to that of single features due to unreasonable feature selection by screening effective features and eliminating invalid features.

[0207] This application aims to solve the difficult problems of identifying targets, range deception jamming, velocity deception jamming, and combined range-velocity deception jamming. First, using the characteristic of whether the number of traces in the range dimension and the Doppler dimension (velocity dimension) changes suddenly, the existence of multiple types of deception jamming is judged, and on this basis, the radar echo traces are divided into four combinations: targets, range deception jamming, velocity deception jamming, and combined range-velocity deception jamming. Then, first, a hierarchical recognition feature set is established in the order of the magnitude of the error in each feature space. By comparing the statistical quantities of the spatial errors, reasonable recognition features are selected, redundant and unreasonable features are eliminated, and the optimized recognition of range deception jamming is completed. Second, a hierarchical recognition feature set is established in the order of the magnitude of the Doppler error of each feature. By comparing the statistical quantities of the Doppler errors, the optimized recognition of velocity deception jamming is completed. Finally, a hierarchical recognition feature set is established in the order of the magnitude of the state estimation error of each feature. By comparing the statistical quantities of the state estimation errors, the optimized recognition of combined range-velocity deception jamming is completed. Finally, multi-moment features are used to comprehensively identify multiple types of deception jamming, identify and suppress the determined deception jamming signals, and retain the uncertain target echo signals.

[0208] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the above method is implemented.

[0209] It should be noted that the user information involved in this application (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) are all information and data that have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

Claims

1. A method for identifying radar range and speed deception interference, characterized in that: include: By using the mutation of the number of traces in the distance dimension and the speed dimension, it is determined that the traces originate from different types of deception interference; the different types of deception interference include distance deception interference, speed deception interference and distance and speed composite deception interference; the traces are discretized representations of target echoes received by the radar; each trace contains the spatial position and speed of the target; the spatial position includes distance and angle; In the distance dimension, for the distance deception interference, different spatial error features at each moment are sorted in order to establish a spatial recognition feature set; the order is from small to large; Determine the optimized distance deception interference and the first uncertain interference based on the space recognition feature set and the preset space decision threshold; In the velocity dimension, for the velocity deception interference, different Doppler error features at each moment are sorted in the order to establish a motion recognition feature set; Determining an optimized speed deception interference and a second uncertain interference based on the motion recognition feature set and a preset motion decision threshold; In the distance and speed dimension, for the distance and speed deception interference, different state estimation error features at each moment are sorted in the order, and a spatial motion recognition feature set is established; Based on the spatial motion recognition feature set and the preset spatial motion decision threshold, determining the optimized distance and speed deception interference and the third uncertain interference; Comprehensively judging the first uncertain interference, the second uncertain interference and the third uncertain interference to determine the target distance and speed deception interference; The optimized range and speed deception interference and the target range and speed deception interference are both used as the finally identified range and speed deception interference.

2. The method for identifying radar range and speed deception interference according to claim 1, characterized in that: Using the mutation of the number of traces in the distance dimension and the speed dimension, it is determined that the traces come from different types of deception interference, including: In a plurality of radar scanning cycles before the current moment, determine the average value of the number of traces at each moment corresponding to the distance dimension, the average value of the number of traces at each moment corresponding to the speed dimension, and the average value of the number of traces at each moment corresponding to the distance and speed dimensions; Determine the distance deception interference according to the number of traces corresponding to the distance dimension in the radar scanning cycle at the current moment and the average number of traces corresponding to the distance dimension at each moment; Determine the speed deception interference according to the number of traces corresponding to the speed dimension in the radar scanning cycle at the current moment and the average number of traces corresponding to the speed dimension at each moment; The range deception interference is determined according to the number of traces corresponding to the range-speed dimension in the radar scanning cycle at the current moment and the average number of traces corresponding to the range-speed dimension at each moment.

3. The method for identifying radar range and speed deception interference according to claim 1, characterized in that: In the distance dimension, for the distance deception interference, different spatial error features at each moment are sorted in order to establish a spatial recognition feature set, which specifically includes: Based on the bistatic radar network and the Cartesian coordinate system, determine the first position information of the target detected by radar A at the current moment and the second position information of the target detected by radar B at the current moment; determining a two-dimensional spatial error feature according to the two-dimensional relative distance between radar A and radar B, the first position information, and the second position information; determining a three-dimensional spatial error feature according to the three-dimensional relative distance between radar A and radar B, the first position information, the second position information, the pitch information corresponding to radar A, and the pitch information corresponding to radar B; Determine a four-dimensional spatial error feature according to the four-dimensional relative distance between radar A and radar B, the first position information, the second position information, the pitch information corresponding to the radar A, the pitch information corresponding to the radar B, the radial velocity corresponding to the radar A, and the radial velocity corresponding to the radar B; the different spatial error features include two-dimensional spatial error features, three-dimensional spatial error features, and four-dimensional spatial error features; Sorting the different spatial error features from small to large, and using the sorted spatial error feature sequence as the first recognition feature set at the current moment; The first recognition feature set at each moment is used as the spatial recognition feature set.

4. The method for identifying radar range and speed deception interference according to claim 1, characterized in that: In the speed dimension, for the speed deception interference, different Doppler error features at each moment are sorted in the order described, and a motion recognition feature set is established, which specifically includes: Determine a two-dimensional radial velocity estimation value at time K according to the two-dimensional radial distances of the radar at time K-1 and time K, and determine a two-dimensional Doppler error characteristic according to the two-dimensional radial velocity estimation value and the two-dimensional radial velocity measurement value; Determine a 3D radial velocity estimate at time K according to the 3D radial distances of the radar at time K-1 and time K, and determine a 3D Doppler error feature according to the 3D radial velocity estimate and the 3D radial velocity measurement; Using target tracking technology, determine the target state vector at time K, and use the target state vector at time K to determine the four-dimensional radial velocity estimate at time K; Determine a four-dimensional Doppler error feature according to the four-dimensional radial velocity estimation value at the time K and the four-dimensional radial velocity measurement value at the time K; the different Doppler error features include a two-dimensional Doppler error feature, a three-dimensional Doppler error feature and a four-dimensional Doppler error feature; Sorting the different Doppler error features from small to large, and using the sorted Doppler error features as the second identification feature set at time K; The second recognition feature set at each moment is used as the motion recognition feature set.

5. The method for identifying radar range and speed deception interference according to claim 1, characterized in that: In the distance and speed dimension, for the distance and speed deception interference, different state estimation error characteristics at each moment are sorted in the order, specifically including: Based on the dual-base radar networking and target tracking technology, the two-dimensional state estimation of radar A and radar B at the moment is determined respectively by using the two-dimensional measurement of the radar; Determining a two-dimensional state estimation error characteristic according to the two-dimensional state estimation of the radar A and the radar B, and a two-dimensional state estimation error matrix of the radar A and the radar B; Using the three-dimensional measurements of the radars, the three-dimensional state estimates of radar A and radar B are determined respectively; Determining a three-dimensional state estimation error characteristic according to the three-dimensional state estimation of the radar A and the radar B and a three-dimensional state estimation error matrix of the radar A and the radar B; Using the four-dimensional measurements of the radars, the four-dimensional state estimates of the radar A and the radar B are determined respectively; Determining a four-dimensional state estimation error characteristic according to the four-dimensional state estimation of the radar A and the radar B, and a four-dimensional state estimation error matrix of the radar A and the radar B; The different state estimation error characteristics include two-dimensional state estimation error characteristics, three-dimensional state estimation error characteristics and four-dimensional state estimation error characteristics; Sorting the different state estimation error features at the current moment from small to large, and using the sorted state estimation error features as the third identification feature set at the current moment; The third recognition feature set at each moment is used as the spatial motion recognition feature set.

6. The method for identifying radar range and speed deception interference according to claim 1, characterized in that: Based on the space identification feature set and the preset space decision threshold, determining the optimized distance deception interference and the first uncertain interference specifically includes: For different spatial error features at any moment, the different spatial error features include two-dimensional spatial error features, three-dimensional spatial error features and four-dimensional spatial error features; When the different spatial error characteristics at the moment meet the first condition, it is determined that the point trace originates from the real target; the first condition is that the different spatial error characteristics are all smaller than the preset spatial decision threshold; When the different spatial error characteristics at the moment meet the second condition, it is determined that the point trace originates from the optimized distance deception interference; the second condition is that the different spatial error characteristics are all greater than or equal to the preset spatial decision threshold; Different spatial error features at each moment that do not satisfy the first condition and the second condition are determined as first uncertain interference.

7. The method for identifying radar range and speed deception interference according to claim 1, characterized in that: Based on the motion recognition feature set and the preset motion decision threshold, the optimized speed deception interference and the second uncertain interference are determined, specifically including: For different Doppler error characteristics at any moment, the different Doppler error characteristics include two-dimensional Doppler error characteristics, three-dimensional Doppler error characteristics and four-dimensional Doppler error characteristics; When the different Doppler error characteristics at the time satisfy the third condition, it is determined that the point trace originates from the real target; the third condition is that the different Doppler error characteristics are all smaller than the preset motion decision threshold; When the different Doppler error characteristics at the moment meet the fourth condition, it is determined that the point trace originates from the optimized speed deception interference; the fourth condition is that the different Doppler error characteristics are all greater than or equal to the preset motion decision threshold; The different Doppler error characteristics at each moment that do not satisfy the third condition and the fourth condition are determined as the second uncertain interference.

8. The method for identifying radar range and speed deception interference according to claim 1, characterized in that: Based on the spatial motion recognition feature set and the preset spatial motion decision threshold, the optimized distance speed deception interference and the third uncertain interference are determined, specifically including: Different state estimation error characteristics at any moment, the different state estimation error characteristics include two-dimensional state estimation error characteristics, three-dimensional state estimation error characteristics and four-dimensional state estimation error characteristics; When the fifth condition is met, it is determined that the point trace originates from the real target; the fifth condition is that the estimation error characteristics of different states are all less than the preset spatial motion decision threshold; When the sixth condition is met, it is determined that the point trace originates from the optimized distance and speed deception interference; the sixth condition is that the different state estimation error characteristics are all greater than or equal to the preset spatial motion decision threshold; Different state estimation error characteristics at each moment that do not satisfy the fifth condition and the sixth condition are determined as the third uncertain interference.

9. The method for identifying radar range and speed deception interference according to claim 1, characterized in that: Comprehensively judging the first uncertain interference, the second uncertain interference, and the third uncertain interference to determine the target distance and speed deception interference specifically includes: Determine a comprehensive statistical decision amount according to the first uncertain interference, the second uncertain interference, the third uncertain interference, and according to a preset spatial decision threshold, a preset motion decision threshold, and a preset spatial motion decision threshold; According to the preset comprehensive decision threshold and the comprehensive statistical decision amount, it is determined whether the target echo is the target distance and speed deception interference.

10. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the radar range and speed deception interference identification method according to any one of claims 1 to 9.

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