A method and device for identifying radar range-velocity deception jamming

Abstract

The present application discloses a method and device for identifying radar range-velocity deception jamming, which relates to the field of anti-jamming of radar networks. The method determines 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; establishes a spatial recognition feature set; determines the optimized range deception jamming and the first uncertain jamming based on the spatial recognition feature set and a preset spatial decision threshold; establishes a motion recognition feature set; determines the optimized velocity deception jamming and the second uncertain jamming based on the motion recognition feature set and a preset motion decision threshold; establishes a spatial motion recognition feature set; determines the optimized range-velocity deception jamming and the third uncertain jamming based on the spatial motion recognition feature set and a preset spatial motion decision threshold; and makes 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. The present application improves the recognition rate of range-velocity deception jamming.

Description

Technical Field

[0001] This 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 Technique

[0002] With the service of new military aircraft, current active jamming devices are further developing 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 decisions. 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 there is little research on the influence of velocity deception on target detection, 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 this 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 following solutions are provided in this application.

[0007] In a first aspect, the present application provides a method for identifying radar range-velocity deception jamming, including: determining that 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 order, and establish a spatial recognition feature set; the order is from small to large. 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 order, and 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 order, and 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:

[0010] 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 type 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 identification feature set with hierarchical features is established. The optimized identification of range deception jamming is completed through the spatial identification feature set. Further, different Doppler error characteristics at each moment are arranged in ascending order, and a motion identification feature set with hierarchical features is established. The optimized identification of velocity deception jamming is completed through the motion identification feature set. Finally, different state estimation error characteristics at each moment are arranged in ascending order, and a spatial-motion identification feature set with hierarchical features is established. The optimized identification of range-velocity compound deception jamming is completed through the spatial-motion identification 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 regarded 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

[0011] 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 for use 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.

[0012] Figure 1 is a flowchart of a method for identifying radar range-velocity deception jamming.

[0013] Figure 2 is a flowchart of a method for identifying range-velocity deception jamming based on hierarchical feature extraction.

[0014] Figure 3 is a diagram of an existence detection scheme for range deception jamming.

[0015] Figure 4 is a diagram of an existence detection scheme for velocity deception jamming.

[0016] Figure 5 is a diagram of an existence detection scheme for range-velocity compound deception jamming.

[0017] Figure 6 It is a two-dimensional spatial error feature extraction diagram.

[0018] Figure 7 It is a step-by-step spatial error feature extraction and screening scheme diagram.

[0019] Figure 8 It is a two-dimensional Doppler error feature extraction diagram.

[0020] Figure 9 It is a step-by-step Doppler error feature extraction and screening scheme diagram.

[0021] Figure 10 It is a step-by-step state estimation error feature extraction and screening scheme diagram.

[0022] Figure 11 It is a comprehensive decision-making on range-velocity composite deception jamming based on multi-moment features. Specific implementation manners

[0023] 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. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0024] 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.

[0025] As Figure 1 and Figure 2 shown, the present application provides a method for identifying radar range-velocity deception jamming, including:

[0026] 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.

[0027] Step 102: 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.

[0028] 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.

[0029] Step 104: In the velocity dimension, for the velocity deception interference, sort different Doppler error characteristics at each moment according to the order, and establish a motion recognition feature set.

[0030] 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.

[0031] Step 106: In the range-velocity dimension, for the range-velocity deception interference, sort different state estimation error characteristics at each moment according to the order, and establish a spatial motion recognition feature set.

[0032] 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.

[0033] 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.

[0034] Step 109: Take both the optimized range-velocity deception interference and the target range-velocity deception interference as the finally recognized range-velocity deception interference.

[0035] Specifically, the process of the present application for recognizing range-velocity deception interference specifically includes the following content.

[0036] 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.

[0037] 2. In the range dimension, establish a hierarchical recognition feature set in the order of the size of each feature space error, select reasonable range deception recognition features through comparison of spatial error statistics, and eliminate redundant and unreasonable features, thereby completing the optimized recognition of range deception interference.

[0038] 3. In the velocity dimension, establish a hierarchical recognition feature set in the order of the size of each feature Doppler error, select reasonable velocity deception recognition features through comparison of Doppler error statistics, and eliminate redundant velocity deception features, thereby completing the optimized recognition of velocity deception interference.

[0039] 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.

[0040] 5. On the basis of the hierarchical recognition of range deception interference, velocity deception interference, and range - velocity combined 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 make a comprehensive decision to determine whether it is a target range - velocity deception interference.

[0041] Among them, the uncertain target echo signals are the first uncertain interference, the second uncertain interference, and the third uncertain interference.

[0042] 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; determine range deception interference according to the number of traces corresponding to the range dimension within the current radar scan cycle and the average number of traces at each moment corresponding to the range dimension; determine velocity deception interference according to the number of traces corresponding to the velocity dimension within the current radar scan cycle and the average number of traces at each moment corresponding to the velocity dimension; determine range deception interference according to the number of traces corresponding to the range - velocity dimension within the current radar scan cycle and the average number of traces at each moment corresponding to the range - velocity dimension.

[0043] In practical applications, the specific process of step 101 is as follows.

[0044] 1. Detection of the existence of deceptive interference based on the mutation of the number of measurement points.

[0045] 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 mutates to judge the existence of range deception interference, velocity deception interference, and range - velocity combined deception interference, and on this basis, divide the radar echo traces into four combinations: target, range deception interference, velocity deception interference, and range - velocity combined deception interference, so as to achieve the refined recognition of multiple types of deception interference.

[0046] 1) Detection of the existence of interference in the range - time dimension.

[0047] For the characteristic that interference appears after the radar has been powered on for a period of time, the existence of range deception interference is detected by using the characteristic of the mutation of measured points in the range-time dimension. Considering that the range-time dimension can not only count the number of measured points in the current radar scanning period, but also count the steady value of the number of measured points in multiple scanning periods, therefore, the statistical value of the number of measured points in the current radar scanning period is compared with the steady value of the number of measured points in multiple radar scanning periods to judge the existence of range deception interference, as Figure 3 shown.

[0048] Suppose at time the number of measured points in the range-time dimension within the radar scanning period is , then the average value of the number of measured points within

[0049] scanning periods is:[[]]END]]

[0050] When the number of measured points within the radar scanning period at time is

[0051] If , it is judged that there is no range deception interference within the current radar scanning period.

[0052] If , it is judged that there is range deception interference within the current radar scanning period.

[0053] Among them, is the decision coefficient.

[0054] 2) Detection of the existence of interference in the Doppler-time dimension (i.e., the velocity dimension).

[0055] The existence of range deception interference is detected by using the characteristic of the mutation of measured points in the Doppler-time dimension. The statistical value of the number of Doppler points in the current radar scanning period is compared with the steady value of the number of Doppler points in multiple radar scanning periods to judge the existence of velocity deception interference, as Figure 4 shown.

[0056] Suppose at time the number of measured points in the Doppler-time dimension within the radar scanning period is , then the average value of the number of measured points within

[0057] scanning periods is:[[]]END]]

[0058] When The number of traces within the radar scan cycle at a certain moment is When this is the case, the existence problem of velocity deception jamming can be further analyzed and judged by the following hypothesis test.

[0059] If , it is judged that there is no velocity deception jamming within the current radar scan cycle.

[0060] If , it is judged that there is velocity deception jamming within the current radar scan cycle.

[0061] 3) Detection of the existence of jamming in the range-velocity two-dimensional plane.

[0062] Utilize the characteristic of the mutation of measured traces in the range-velocity two-dimensional plane to detect the existence of range-velocity composite deceptive jamming. Compare the statistical value of the number of range-velocity two-dimensional points in the current radar scan cycle with the stable value of the number of range-velocity two-dimensional points in multiple radar scan cycles to judge the existence of range-velocity composite deceptive jamming, as Figure 5 shown.

[0063] Suppose at the moment the number of range-velocity two-dimensional points within the radar scan cycle is , then the average value of the number of range-velocity two-dimensional points within

[0064] scan cycles is:

[0065] When the number of range-velocity two-dimensional points within the radar scan cycle at a certain moment is , the existence problem of range-velocity composite deceptive jamming can be further analyzed and judged by the following hypothesis test.

[0066] If , it is judged that there is no range-velocity deception jamming within the current radar scan cycle.

[0067] If , it is judged that there is range-velocity composite deceptive jamming within the current radar scan cycle.

[0068] 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 feature 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 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; determining the 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 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 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 smallest to largest, and using the sorted spatial error feature 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.

[0069] 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 feature 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 feature 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 feature 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 features include two-dimensional Doppler error features, three-dimensional Doppler error features, and four-dimensional Doppler error features; sorting the different Doppler error features from smallest to largest, and using the sorted Doppler error features as the second recognition feature set at moment K; using the second recognition feature set at each moment as the motion recognition feature set.

[0070] 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 determine 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, determine the two-dimensional state estimation error characteristics; Using the three-dimensional measurements of the radar, respectively determine 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, determine the three-dimensional state estimation error characteristics; Using the four-dimensional measurements of the radar, respectively determine 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, determine 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; Arrange the different state estimation error characteristics at the current moment in ascending order, and use the sorted state estimation error characteristics as the third recognition feature set at the current moment; Use the third recognition feature sets at each moment as the spatial motion recognition feature set.

[0071] 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, determine 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, determine 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, determine it as a first uncertain interference.

[0072] 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, determine 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, determine 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, determine it as a second uncertain interference.

[0073] In some embodiments, step 107 specifically includes: for 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, determining that the trace originates from a real target; the fifth condition being that the different state estimation error characteristics are all less than a preset spatial motion decision threshold; when the sixth condition is satisfied, determining that the trace originates from an optimized range-velocity deception interference; the sixth condition being 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, determining it as a third uncertain interference.

[0074] 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.

[0075] In practical applications, the specific processes of steps 102 - 109 are as follows.

[0076] 1. Recognition of successive range deception interference based on the magnitude of spatial error.

[0077] 1) Extraction of two-dimensional spatial error characteristics.

[0078] Based on the bistatic radar network, assume the measurement of radar A at time is , and the measurement of radar B is

[0079] .

[0080] 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.

[0081] Correspondingly, the measurement error matrices respectively 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.

[0082] 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 .

[0083] .

[0084] 2) Extraction of three-dimensional spatial error features.

[0085] Based on 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:

[0086] .

[0087] Among them, is the first position information of the target detected by radar A in the three-dimensional space at moment; is the second position information of the target detected by radar B in the three-dimensional space at moment; and are the pitch information measured by radar A and radar B respectively at moment.

[0088] 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 :

[0089] .

[0090] Among them, and are respectively and three-dimensional measurement error matrices, is the three-dimensional relative distance between radars A and B.

[0091] 3) Extraction of four-dimensional spatial error features.

[0092] Based on 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.

[0093] .

[0094] Among them, The first position information of the target detected by the four-dimensional space radar A at the moment; The second position information of the target detected by the four-dimensional space radar B at the moment.

[0095] By using the difference in the four-dimensional space characteristics between the range deception and the real target in the dual radars, the four-dimensional space error characteristics can be extracted:

[0096] .

[0097] Among them, and are respectively the radial velocity measurements of the radar A and the radar B at the moment, and are respectively and the four-dimensional measurement error matrices of, is the four-dimensional relative distance between the radar A and the radar B.

[0098] 4) Sorting and selection of the space error characteristics.

[0099] Sort the different space 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.

[0100] 5) Optimization and recognition of the range deception interference.

[0101] For the selected space error characteristics, establish a multi-level decision-making mechanism according to the number of uses. When the minimum characteristic is selected for decision-making, it is the first-level decision; when the two minimum characteristics are selected for decision-making, it is the second-level decision; when the three minimum characteristics are selected for decision-making, it is the third-level decision; and so on.

[0102] a) First-level decision.

[0103] On the above basis, the problem of range deception interference recognition and suppression can be optimized and judged by the following hypothesis test (assuming is the minimum value).

[0104] When , it is judged as the real distance.

[0105] When , it is judged as range deception and eliminated.

[0106] 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.

[0107] b) Second-level decision.

[0108] Based on the above, the problem of distance deception interference identification and suppression can be optimized by the following hypothesis test (assuming and are the two smallest values) as follows.

[0109] When and hold, the decision is a true distance.

[0110] When and hold, the decision is distance deception and it is excluded.

[0111] In other cases, it is uncertain interference and is sent for comprehensive decision at multiple times.

[0112] 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.

[0113] c) Third-level decision.

[0114] Based on the above, the problem of distance deception interference identification and suppression can be optimized by the following hypothesis test as follows.

[0115] When and and hold, the decision is a true distance.

[0116] When and and hold, the decision is distance deception and it is excluded.

[0117] In other cases, it is uncertain interference and is sent for comprehensive decision at multiple times.

[0118] Among them, 、 and are the third-level decision statistics after screening, 、 and are the third-level decision thresholds, which can be obtained by using Testing the acquisition of statistical knowledge.

[0119] 3. Recognition of step-by-step range deception jamming based on the magnitude of Doppler error.

[0120] 1) Extraction of two-dimensional Doppler error features.

[0121] Assume time and the two-dimensional radial distances of the radar at time are and , then the radial velocity estimation at time can be expressed as:

[0122] .

[0123] By utilizing the difference in two-dimensional Doppler features between velocity deception and real targets, the two-dimensional Doppler error feature can be extracted, as shown in Figure 8 .

[0124] .

[0125] Among them, is the measured two-dimensional radial velocity, T is the sampling interval, and are the radial velocity estimation error and the radial velocity measurement error respectively.

[0126] 2) Extraction of three-dimensional Doppler error features.

[0127] Assume time and the three-dimensional radial distances of the radar at time are and , then the radial velocity estimation at time can be expressed as:

[0128] .

[0129] By utilizing the difference in three-dimensional Doppler features between velocity deception and real targets, as shown in Figure 9 , the three-dimensional Doppler error feature can be extracted as follows.

[0130] .

[0131] Among them, is the measured three-dimensional radial velocity, T is the sampling interval, and are the radial velocity estimation error and the radial velocity measurement error respectively.

[0132] 3) Extraction of four-dimensional Doppler error characteristics.

[0133] Suppose the target state vector obtained by using target tracking technology at time is:

[0134] .

[0135] Among them, , , and are respectively the direction position estimation, direction velocity estimation, direction position estimation, direction velocity estimation of target tracking.

[0136] Then its radial velocity estimation at time can be expressed as:

[0137] .

[0138] By using the four-dimensional Doppler feature differences between velocity deception and real targets, as Figure 9 shown, the four-dimensional Doppler error characteristics can be extracted:

[0139] .

[0140] 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.

[0141] 4) Sorting and selection of Doppler error characteristics.

[0142] Sort 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 relatively large errors to solve the misjudgment or missed judgment problems caused by characteristic redundancy or unreasonable characteristic selection, as Figure 9 shown.

[0143] 5) Optimal identification of velocity deception interference.

[0144] For the selected Doppler error features, a multi-level decision-making mechanism is established according to the usage quantity. When the minimum feature is selected for decision-making, it is the first-level decision; when the two minimum features are selected for decision-making, it is the second-level decision; when the three minimum 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 second uncertain interference.

[0145] a) First-level decision.

[0146] On the above basis, the problem of range deception interference recognition and suppression can be optimized by the following hypothesis test (assuming is the minimum value).

[0147] When , it is judged as the true speed.

[0148] When , it is judged as speed deception and excluded.

[0149] 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 testing.

[0150] b) Second-level decision.

[0151] On the above basis, the problem of range deception interference recognition and suppression can be optimized by the following hypothesis test (assuming and are the two minimum values).

[0152] When and , it is judged as the true speed.

[0153] When and , it is judged as speed deception and excluded.

[0154] In other cases, it is uncertain interference and sent for comprehensive decision-making at multiple times.

[0155] 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 testing.

[0156] c) Third-level decision.

[0157] On the above basis, the problem of range deception interference recognition and suppression can be optimized by the following hypothesis test.

[0158] When and and , the judgment is the true speed.

[0159] When and and , the judgment is speed deception and it is eliminated.

[0160] In other cases, it is uncertain interference and is sent for comprehensive judgment at multiple moments.

[0161] Among them, , and are the three - level judgment statistics after screening, , and are the three - level judgment thresholds and can be obtained by using statistical knowledge of tests.

[0162] 4. Recognition of progressive range - speed composite deception interference based on the magnitude of state - estimation error.

[0163] 1) Extraction of two - dimensional state - estimation error features.

[0164] Based on the bistatic radar network, through existing target - tracking technologies, the state estimate can be obtained using the two - dimensional measurements of radar A, and the state estimate can be obtained using the two - dimensional measurements of radar B. At this time, by using the differences in the state - estimation error features of range - speed deception and real targets in the two radars, as shown in Figure 10 , the two - dimensional state - estimation error features can be extracted:

[0165] .

[0166] Among them, and are respectively and state - estimation error matrices and can be obtained during the target - tracking process.

[0167] 2) Extraction of three - dimensional state - estimation error features.

[0168] Based on the extraction of two - dimensional state - estimation error features, further introduce the elevation information. Through existing target - tracking technologies, the state estimate can be obtained using the three - dimensional measurements of radar A, and the state estimate can be obtained using the three - dimensional measurements of radar B. At this time, by using the differences in the state - estimation error features of range - speed composite deception and real targets in the two radars, asFigure 10 As shown, the three-dimensional state estimation error features can be extracted .

[0169] .

[0170] Among them, and are respectively and state estimation error matrices, which can be obtained during the target tracking process.

[0171] 3) Extraction of four-dimensional state estimation error features.

[0172] On the basis of the extraction of four-dimensional state estimation error features, 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, using the differences in the state estimation error features of the range-velocity composite deception and the real target in the dual radars, as Figure 10 shown, the four-dimensional state estimation error features can be extracted :[[]]END]]

[0173] .

[0174] Among them, and are respectively and state estimation error matrices, which can be obtained during the target tracking process.

[0175] 4) Sorting and selection of different state estimation error features.

[0176] 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.

[0177] 5) Optimization and identification of range-velocity interference.

[0178] For the selected different state estimation error features, a multi-level decision-making mechanism is established according to the usage quantity. When the smallest feature is selected for decision-making, it is the first-level decision; when the smallest two features are selected for decision-making, it is the second-level decision; when the smallest three 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.

[0179] a) Primary decision.

[0180] Based on the above, the problem of deception jamming identification and suppression can be optimized by the following hypothesis test (assuming is the minimum value).

[0181] When it is determined as a real target.

[0182] When it is determined as deception jamming and excluded.

[0183] Among them, is the primary decision statistic after screening, is the primary decision threshold, which can be obtained by using statistical knowledge for testing.

[0184] b) Secondary decision.

[0185] Based on the above, the problem of deception jamming identification and suppression can be optimized by the following hypothesis test (assuming and are the two minimum values).

[0186] When and it is determined as a real target.

[0187] When and it is determined as deception jamming and excluded.

[0188] In other cases, it is uncertain jamming and sent for comprehensive decision at multiple moments.

[0189] Among them, and are the secondary decision statistics after screening, and are the secondary decision thresholds, which can be obtained by using statistical knowledge for testing.

[0190] c) Tertiary decision.

[0191] Based on the above, the problem of deception jamming identification and suppression can be optimized by the following hypothesis test.

[0192] When and and it is determined as the real distance.

[0193] When and and When it is time, the decision is distance deception and it is eliminated.

[0194] In other cases, it is uncertain interference and is sent for comprehensive decision-making at multiple times.

[0195] 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.

[0196] 5. Comprehensive decision-making on range-velocity deception jamming based on multi-time characteristics.

[0197] To effectively improve the correct recognition probability of range-velocity composite deception jamming, based on the decision-making at a single time, the feature differences at multiple times are used to further identify range-velocity composite deception jamming, as Figure 11 shown.

[0198] The recognition results of range-velocity deception jamming at different times are input into the comprehensive decision-making system, and then the range-velocity composite deception jamming is analyzed and discriminated centrally in the comprehensive decision-making system using the scoring method (taking the first-level decision-making situation as an example) as follows.

[0199] If and and , then .

[0200] If and and , then .

[0201] If and and , then .

[0202] If and and , then .

[0203] If and and then .

[0204] If and and then .

[0205] If and and then 。

[0206] If and and then 。

[0207] On the basis of making a preliminary judgment on the range-velocity deception jamming by using different spatial error characteristics, different Doppler error characteristics, and different state estimation error characteristics, let 。

[0208] Among them, is the comprehensive statistical decision-making quantity at multiple moments, is the comprehensive statistical decision-making quantity at the moment.

[0209] The problem of identifying range-velocity composite jamming based on multiple moments can be further analyzed and judged by the following hypothesis test.

[0210] If , then the target echo originates from range-velocity deception jamming.

[0211] If , then the target echo originates from a real target.

[0212] Among them, is the comprehensive decision threshold, which is determined by the observation duration.

[0213] Specifically, the purpose of this application is to break through the shackles of traditional radar anti-jamming methods, solve the problems of identifying targets, range deception jamming, velocity deception jamming, and range-velocity deception jamming, 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.

[0214] 1) The problem of the existence test of multiple types of range deception and velocity deception jamming under the condition that the jamming pattern is unknown.

[0215] 2) The problem of the low positive recognition probability caused by the 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.

[0216] 3) In multi-feature recognition, the problem that the multi-feature recognition effect is worse than that of single feature due to feature redundancy.

[0217] 4) In multi-feature recognition, the problem that the multi-feature recognition effect is worse than that of single feature due to the inappropriate order of feature use.

[0218] Compared with the related technologies, the new method for identifying distance-velocity composite deception jamming based on hierarchical feature extraction described in this application has the following beneficial effects:

[0219] 1) Most of the related technologies are based on the assumption that the jamming patterns are known, and they identify and suppress the known jamming patterns accordingly. However, this application is based on the situation where the jamming patterns are unknown. First, it conducts the existence test of different jamming patterns, and then conducts targeted identification and suppression, which is more in line with the actual situation.

[0220] 2) This application is an improvement on the existing methods for identifying distance deception jamming. This method establishes a hierarchical recognition feature set in the order of the error magnitudes of each feature space. By comparing the statistical quantities of the space errors, reasonable recognition features can be selected, and redundant and unreasonable features can be eliminated, thereby completing the optimized recognition of distance deception jamming.

[0221] 3) This application is an improvement on the existing methods for identifying velocity deception jamming. This method establishes a hierarchical recognition feature set in the order of the Doppler error magnitudes of each feature. By comparing the statistical quantities of the Doppler errors, reasonable recognition features can be selected, and redundant and unreasonable features can be eliminated, thereby completing the optimized recognition of velocity deception jamming.

[0222] 4) This application is an improvement on the existing methods for identifying distance-velocity composite deception jamming. This method establishes a hierarchical recognition feature set in the order of the state estimation error magnitudes of each feature. By comparing the statistical quantities of the state estimation errors, reasonable recognition features can be selected, and redundant and unreasonable features can be eliminated, thereby completing the optimized recognition of distance-velocity composite deception jamming.

[0223] 5) The hierarchical feature recognition technology proposed in this application can solve the problem that the recognition effect of multiple features is worse than that of a single feature due to unreasonable feature selection by screening effective features and eliminating invalid features.

[0224] This application aims to solve the difficult problems of target, range deception jamming, velocity deception jamming, and range-velocity deception jamming recognition. First, by using the characteristic of whether the number of traces in the range dimension and Doppler dimension (velocity dimension) mutates, the existence of multiple types of deception jamming is judged, and on this basis, the radar echo traces are divided into four combinations: target, range deception jamming, velocity deception jamming, and range-velocity combined deception jamming. Then, first, a hierarchical recognition feature set is established in the order of the error magnitudes of each feature space. By comparing the spatial error statistics, reasonable recognition features are selected, and redundant and unreasonable features are eliminated to complete the optimized recognition of range deception jamming. Second, a hierarchical recognition feature set is established in the order of the Doppler error magnitudes of each feature. By comparing the Doppler error statistics, the optimized recognition of velocity deception jamming is completed. Finally, a hierarchical recognition feature set is established in the order of the state estimation error magnitudes of each feature. By comparing the state estimation error statistics, the optimized recognition of range-velocity deception jamming is completed. Finally, multi-moment features are used to comprehensively identify multiple types of deception jamming, the identified deception jamming signals are recognized and suppressed, and the uncertain target echo signals are retained.

[0225] 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.

[0226] It should be noted that the user information (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.) involved in this application are all information and data 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-velocity deception jamming, characterized in that Including: 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 deceptive interferences; the different types of deceptive interferences include range deception interference, velocity deception interference, and range-velocity deception interference; the traces are the discretized 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 interference, sort the different spatial error characteristics at each moment in sequence, and establish a spatial recognition feature set; the sequence is from small to large. Based on the spatial recognition feature set and a preset spatial decision threshold, determine the optimized range deception interference and the first uncertain interference. In the velocity dimension, for the velocity deception interference, sort the different Doppler error characteristics at each moment in the said sequence, and establish a motion recognition feature set. Based on the motion recognition feature set and a preset motion decision threshold, determine the optimized velocity deception interference and the second uncertain interference. In the range-velocity dimension, for the range-velocity deception interference, sort the different state estimation error characteristics at each moment in the said sequence, and 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 interference and the third uncertain interference. Perform a comprehensive decision on the first uncertain interference, the second uncertain interference, and the third uncertain interference to determine the target range-velocity deception interference. Regard both the optimized range-velocity deception interference and the target range-velocity deception interference as the finally recognized range-velocity deception interference.

2. The method for identifying radar range-velocity deception jamming according to claim 1, characterized in that, 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 deceptive interferences, specifically including: Within multiple radar scanning cycles before the current moment, determine the average value of the number of traces at each moment corresponding to the range dimension, the average value of the number of traces at each moment corresponding to the velocity dimension, and the average value of the 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 scanning cycle and the average value of the number of traces at each moment corresponding to the range dimension, determine the range deception interference. According to the number of traces corresponding to the velocity dimension in the current radar scanning cycle and the average value of the number of traces at each moment corresponding to the velocity dimension, determine the velocity deception interference. According to the number of traces corresponding to the range-velocity dimension in the current radar scanning cycle and the average value of the number of traces at each moment corresponding to the range-velocity dimension, determine the range-velocity deception interference.

3. The method for identifying radar range-velocity deception jamming according to claim 1, characterized in that, In the range dimension, for the range deception interference, sort the different spatial error characteristics at each moment in sequence to establish a spatial recognition feature set, specifically including: Based on the bistatic radar networking 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. According to the two-dimensional relative distance between radar A and radar B, the first position information, and the second position information, determine the two-dimensional spatial error characteristics. Determine the three-dimensional space error characteristics based on 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 the four-dimensional space error characteristics based on 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 space error characteristics include two-dimensional space error characteristics, three-dimensional space error characteristics, and four-dimensional space error characteristics; Sort the different space error characteristics from smallest to largest, and use the sorted space error characteristic sequence as the first recognition feature set at the current moment; Use the first recognition feature set at each moment as the space recognition feature set.

4. The method for identifying radar range-velocity deception jamming according to claim 1, characterized in that In the velocity dimension, for the velocity deception interference, sort the different Doppler error characteristics at each moment in the order, and establish a motion recognition feature set, specifically including: Determine the estimated value of the two-dimensional radial velocity at time K based on the two-dimensional radial distances of the radar at time K-1 and time K, and determine the two-dimensional Doppler error characteristic based on the estimated value of the two-dimensional radial velocity and the measured value of the two-dimensional radial velocity; Determine the estimated value of the three-dimensional radial velocity at time K based on the three-dimensional radial distances of the radar at time K-1 and time K, and determine the three-dimensional Doppler error characteristic based on the estimated value of the three-dimensional radial velocity and the measured value of the three-dimensional radial velocity; Use the target tracking technology to determine the target state vector at time K, and use the target state vector at time K to determine the estimated value of the four-dimensional radial velocity at time K; Determine the four-dimensional Doppler error characteristic based on the estimated value of the four-dimensional radial velocity at time K and the measured value of the four-dimensional radial velocity at time K; The different Doppler error characteristics include two-dimensional Doppler error characteristics, three-dimensional Doppler error characteristics, and four-dimensional Doppler error characteristics; Sort the different Doppler error characteristics from smallest to largest, and use the sorted Doppler error characteristics as the second recognition feature set at time K; Use the second recognition feature set at each moment as the motion recognition feature set.

5. The recognition method of radar range-velocity deception jamming according to claim 1, characterized in that In the range-velocity dimension, for the range-velocity deception interference, sort the different state estimation error characteristics at each moment in the order, specifically including: Based on the bistatic radar networking and target tracking technology, use the two-dimensional measurements of the radar to respectively determine the two-dimensional state estimations of radar A and radar B at the moment; Determine the two-dimensional state estimation error characteristic based on the two-dimensional state estimations of radar A and radar B and the two-dimensional state estimation error matrix of radar A and radar B; Use the three-dimensional measurements of the radar to respectively determine the three-dimensional state estimations of radar A and radar B; Determine the three-dimensional state estimation error characteristic based on the three-dimensional state estimations of radar A and radar B and the three-dimensional state estimation error matrix of radar A and radar B; Use the four-dimensional measurements of the radar to respectively determine the four-dimensional state estimations of radar A and radar B; Determine the four-dimensional state estimation error characteristics based on the four-dimensional state estimations of radar A and radar B, and the four-dimensional state estimation error matrices of radar A and 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. Sort the different state estimation error characteristics at the current moment from smallest to largest, and use the sorted state estimation error characteristics as the third recognition feature set at the current moment. Use the third recognition feature sets at each moment as the spatial motion recognition feature set.

6. The recognition method of radar range-velocity deception jamming according to claim 1, characterized in that, Determine the optimized range deception interference and the first uncertain interference based on the spatial recognition feature set and a preset spatial decision threshold, specifically including: 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 different spatial error characteristics at the moment satisfy the first condition, determine that the trace originates from a real target; the first condition is that the different spatial error characteristics are all less than the preset spatial decision threshold. When the different spatial error characteristics at the moment satisfy the second condition, determine that the trace originates from the 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. Determine the different spatial error characteristics at each moment that do not satisfy both the first condition and the second condition as the first uncertain interference.

7. The identification method of radar range-velocity deception jamming according to claim 1, characterized in that, Determine the optimized velocity deception interference and the second uncertain interference based on the motion recognition feature set and a preset motion decision threshold, 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 moment satisfy the third condition, determine that the trace originates from a real target; the third condition is that the different Doppler error characteristics are all less than the preset motion decision threshold. When the different Doppler error characteristics at the moment satisfy the fourth condition, determine that the trace originates from the 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. Determine the different Doppler error characteristics at each moment that do not satisfy both the third condition and the fourth condition as the second uncertain interference.

8. The method for identifying radar range-velocity deception jamming according to claim 1, characterized in that Determine the optimized range-velocity deception interference and the third uncertain interference based on the spatial motion recognition feature set and a preset spatial motion decision threshold, specifically including: For 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 satisfied, determine that the trace originates from a real target; the fifth condition is that the different state estimation error characteristics are all less than the preset spatial motion decision threshold. When the sixth condition is satisfied, determine that the trace originates from the 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. The 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 velocity deception jamming according to claim 1, characterized in that Performing comprehensive decision-making on the first uncertain interference, the second uncertain interference, and the third uncertain interference to determine the target range-velocity deception interference, specifically including: 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.

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

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