Anti-main lobe multi-interference processing method based on multi-sub-pulse variable parameter time-sensitive waveform

Abstract

The invention discloses an anti-main lobe multi-interference processing method based on a multi-sub-pulse variable parameter time-sensitive waveform, is applied to the field of radar anti-interference, and aims to solve the problem that a false target induced by ISRJ and a false target induced by distance sidelobe exist in the prior art. According to the method, firstly, on the basis of ideal correlation characteristics of CCCs, a variable parameter time-sensitive multi-sub-pulse waveform of non-uniform agile sub-pulse width and frequency is designed as a radar transmitting signal; then establishing a composite ISRJ model, and adding the composite ISRJ model to a radar echo signal; and finally, carrying out two-stage interference identification and elimination processing based on RVMPV analysis on the generated radar echo signal. Compared with a traditional anti-interference method, the method does not need prior information of interference and is effective in the composite ISRJ; the waveform structure and the anti-interference method are suitable for resisting main lobe composite ISRJ, and the maximum energy loss can be limited within a single sub-pulse.

Description

Technical Field

[0001] This invention belongs to the field of radar anti-jamming, and specifically relates to an anti-jamming technology for countering composite ISRJ. Background Technology

[0002] Radar plays a crucial role in modern electronic warfare. However, with the rapid development of active jamming technology, radar systems face serious jamming threats. One of the most prominent jamming techniques is ISRJ (Independent Target Recognition Jamming), which can generate multiple false targets at the radar terminal, achieving both deception and suppression effects, thereby severely weakening radar effectiveness.

[0003] Recent research on ISRJ suppression methods has focused on directly identifying false target peaks in ISRJ-affected signals by utilizing discriminative features between the interference and the true target echoes. These methods have proven to be more efficient than those relying on interference parameter estimation because they avoid errors caused by imperfect parameter extraction. The main identification frameworks include time-domain analysis, time-frequency analysis, and range-Doppler analysis.

[0004] Time-domain techniques utilize the inherent discontinuities of ISRJ in the time domain, typically by performing integral decomposition on the pulse-compressed received signal. For example, the literature [W. Wu, J. Zou, J. Chen, S. Xu, and Z. Chen, “False-target recognition against interrupted-sampling repeater jamming based on integration decomposition,” IEEE Trans. Aerosp. Electron.Syst., vol. 57, no.5, pp. 2979–2991, 2021.] segments the pulse compression integration process and introduces piecewise cumulative variance to distinguish between real and false targets. The time-domain discontinuities of ISRJ also manifest as fixed characteristics in the frequency domain, prompting the use of time-frequency transforms (such as short-time Fourier transform and wavelet transform) to extract ISRJ features. For example, the literature [J. Dai, X. Hao, X. Yan and Z. Li, “Adaptive false-target recognition for the proximity sensor based on joint-feature extraction and chaotic encryption,” IEEE Sens. J., vol. 22, no. 11, pp. 10828-10840, 2022.] analyzes the difference in ambiguity functions between the real target and the ISRJ, and proposes to derive the range-Doppler spectrum for recognition through Doppler compensation. Although these methods are effective in single interference scenarios, they exhibit limited robustness in complex interference environments. Deep learning-based solutions alleviate this limitation by jointly modeling temporal, spectral, and energy features. However, such neural networks require significant computational resources and large labeled datasets.

[0005] The literature [H. Qiu, X. Yu, G. Cui, J. Yang, and L. Kong, “Wideband LPI radarsubpulse waveform design, processing, and analysis”, IEEE Trans. Aerosp. Electron. Syst., vol. 61, no. 1, pp. 416-432, 2025.] proposes a multi-subpulse waveform whose flexible intra-pulse parameters (i.e., subpulse width, bandwidth, amplitude, and frequency) provide an effective method for combating intra-pulse forwarding mechanisms of ISRJ. Researchers have applied intra-pulse segmentation coding techniques to suppress ISRJ. For example, the literature [Y. Li, J. Wang, Y. Wang, P. Zhang, and L. Zuo, “Randomfrequency-coded waveform optimization and signal coherent accumulation against compound deception jamming”, IEEE Trans. Aerosp. Electron. Syst., vol. 59, no. 4, pp. 4434-4449, 2023] proposes a joint inter-pulse and intra-pulse frequency-coded waveform, which achieves coherent accumulation through sub-pulse rearrangement compensation and non-uniform discrete Fourier transform. However, spectral leakage between sub-pulses and residual interference under high interference sampling ratios reduce the number of interference-free sub-pulses, thus severely degrading the anti-interference performance. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention proposes a main lobe multi-interference processing method based on multi-subpulse variable parametric time-sensitive waveforms to combat main lobe composite ISRJ (Interrupted Sampling Repeater Jamming). It also proposes a two-stage false target identification and elimination method based on RVMPV (Range-Velocity Matching and Peak Variance) analysis, which can distinguish between real targets and false targets caused by ISRJ and range sidelobes without requiring prior knowledge of interference.

[0007] The technical solution adopted in this invention is: a method for processing main lobe multi-interference based on multi-subpulse variable parametric time-sensitive waveforms, comprising:

[0008] S1. Utilizing the ideal correlation characteristics of CCCs, the transmit waveform with low sidelobes and favorable for separation of multiple pulse signals is designed as follows:

[0009]

[0010] In the formula, For the first The baseband signal waveform based on CCCs for each sub-pulse. For the first The pulse width of each sub-pulse Let Heaviside be the unit step function. For frequency hopping interval, It is a frequency hopping code sequence. , Indicates the number of sub-pulses;

[0011] S2. Based on the different forwarding methods, establish a composite ISRJ model and add it to the radar echo signal;

[0012] S3. Perform two-stage interference identification and elimination processing on the radar echo signal generated in step S2 based on RVMPV analysis.

[0013] The beneficial effects of this invention are as follows: First, this invention designs a variable parametric time-sensitive multi-subpulse waveform based on the non-uniform agile subpulse width and frequency of CCCs. Based on this waveform structure, a two-stage false target identification and elimination method based on RVMPV analysis is proposed. Compared with traditional anti-interference methods, this method does not require prior information about the interference and is effective in composite ISRJ. The waveform structure and anti-interference method are suitable for combating main lobe composite ISRJ and can limit the maximum energy loss to a single subpulse. Simulation results show that the waveform design and echo processing method of this invention are suitable for combating main lobe composite ISRJ. Attached Figure Description

[0014] Figure 1 This is a flowchart of the processing based on RVMPV analysis.

[0015] Figure 2 The present invention provides time-domain waveforms and spectrograms of time-sensitive multiple subpulse waveforms with variable parametric width and frequency for non-uniform agile subpulse in an embodiment of the invention.

[0016] Among them, (a) is the time-domain waveform and (b) is the spectrum.

[0017] Figure 3 This is a time-domain waveform diagram of radar signal and composite interference signal echo in an embodiment of the present invention.

[0018] Figure 4 This is a comparison chart of the peak value changes of the real target and the false target after pulse compression peak compensation according to an embodiment of the present invention;

[0019] Among them, (a) is the peak image of real and false targets before the first detection plane compensation, (b) is the peak image of real and false targets after the first detection plane compensation, (c) is the peak image of real and false targets before the second detection plane compensation, (d) is the peak image of real and false targets after the second detection plane compensation, (e) is the peak image of real and false targets before the third detection plane compensation, and (f) is the peak image of real and false targets after the third detection plane compensation.

[0020] Figure 5 This is a target detection planar diagram of slow-time coherent accumulation, constant false alarm rate detection, and dot clustering in an embodiment of the present invention;

[0021] Among them, (a) is the target detection map of detection plane one, and (b) is the target detection map of detection plane two.

[0022] Figure 6 This is a detection planar view after the range-velocity matching (RVM) processing in an embodiment of the present invention.

[0023] Figure 7 This is a detection plane diagram after peak variance (PV) analysis processing according to an embodiment of the present invention. Detailed Implementation

[0024] To facilitate understanding of the technical content of this invention by those skilled in the art, the following description, in conjunction with the accompanying drawings, further illustrates the invention.

[0025] This invention proposes a non-uniform, agile sub-pulse width and frequency-variable parametric time-sensitive multi-sub-pulse waveform, and based on this waveform structure, proposes a corresponding anti-interference processing method. Compared with traditional anti-interference processing methods, this method does not require prior information about the interference and limits energy loss to a single sub-pulse. This waveform design and anti-interference processing method are suitable for combating main lobe composite ISRJ interference. Figure 1 As shown, the implementation process of the method of the present invention includes the following steps:

[0026] Step 1: Utilizing the ideal correlation properties of CCCs (Completely Complementary Coded Sequences), design a transmit waveform with low sidelobes and favorable for multi-pulse signal separation:

[0027] (1)

[0028] In the formula, Indicates time, For the first The pulse width of each sub-pulse Let Heaviside be the unit step function. For frequency hopping interval, It is a frequency hopping code sequence. Indicates the number of sub-pulses. For the first A baseband signal based on CCCs for each sub-pulse, such as a baseband signal based on (4, 4, 3)-CCCs, is as follows:

[0029] (2)

[0030] Step 2: Establish the main lobe composite interference echo signal model:

[0031] The interference system first uses a periodic sampling function Interruption sampling is performed, followed by store-and-forward. The periodic sampling process can be represented as:

[0032] (3)

[0033] in,

[0034] (4)

[0035] in For window functions, The pulse width of the sampled signal. Indicates the repetition interval of the sampled signal. The number of repetitions. This represents a convolution operation, where s is the sampling pulse number. The number of sampling pulses, This represents the Kronecker function.

[0036] Depending on the forwarding method, ISRJ can be divided into Interrupted Samples Direct Repeater Jamming (ISDRJ), Interrupted Samples Periodic Repeater Jamming (ISRRJ), and Interrupted Samples Cyclic Repeater Jamming (ISCRJ). Their mathematical expressions are as follows:

[0037] (5)

[0038] (6)

[0039] (7)

[0040] in and These are the number of repeated forwardings within the sampling period in ISRRJ and ISCRJ, respectively. For repeated forwarding sequence numbers, in formula (6) In formula (7) .

[0041] Assume the radar carrier frequency is The pulse repetition interval (PRI) is... If there are Q pulses within a coherent processing interval (CPI), then the first pulse... The transmitted waveform can be represented as:

[0042] (8)

[0043] Consider the following scenario: a radar is jammed by a target self-defense jammer during single-target tracking. This jammer is capable of transmitting a composite ISRJ (In-Site Reflection Jet). Then, the radar receives the first... The target echo generated by the transmitted waveform can be represented as:

[0044] (9)

[0045] in, Indicates the distance delay caused by the target. and These are the target's distance and speed, respectively. m / s is the speed of light. (The formula is missing from the original text.) In Replace with And substitute into the formula - This allows us to obtain the specific expressions for ISDRJ, ISRRJ, and ISCRJ. For ease of analysis, the different ISRJs are uniformly represented as... Then the first The ISRJ interference signal of each transmitted waveform can be represented as:

[0046] (10)

[0047] This represents the total number of interferences. Then, the radar receives the [number]th [interference signal]. The echo signal is

[0048] (11)

[0049] This indicates that the mean is zero and the variance is... Complex Gaussian white noise.

[0050] Step 3: Two-stage ISRJ identification and elimination based on RVMPV:

[0051] Step 3-1: Frequency Domain Filtering

[0052] Since the sub-pulses are separated in the frequency domain, we first design Each sub-pulse is blocked by a band-stop filter to obtain... Group filtering results. Among them, the first... Each filter is represented as

[0053] (12)

[0054] in, Indicates frequency, , It is the bandwidth of the sub-pulse.

[0055] Step 3-2: Pulse Compression

[0056] Then to The filtered results of the first group are subjected to pulse compression processing. A pulse compression filter is represented as

[0057] (13)

[0058] (14)

[0059] in, Indicates conjugate, the first The first echo passed the first After frequency domain filtering, the pulse compression result is as follows:

[0060] (15)

[0061] and These represent the Fourier transform and the inverse Fourier transform, respectively.

[0062] Step 3-3: Peak Compensation

[0063] Due to the non-uniform variation of sub-pulse widths, the pulse compression gain corresponding to different sub-pulse combinations is unequal, resulting in unequal target peak values. The following section compensates for the pulse compression peak point. It is known that... When all sub-pulses are compressed simultaneously, the output peak value is The sum of the peak values ​​of each sub-pulse, i.e.

[0064] (16)

[0065] in, This represents the modulation amplitude of the nth sub-pulse, corresponding to the filtering out of the nth sub-pulse. After each sub-pulse, the loss value of the pulse compression output peak is

[0066] (17)

[0067] Compensation is performed on the pulse compression results.

[0068] (18)

[0069] After compensation, the target peak value can be expressed as

[0070] (19)

[0071] Steps 3-4: Slow-time coherent accumulation

[0072] Since coherent accumulation does not change the difference between the peak values ​​of the real and spurious targets, further, along Coherent accumulation is performed on the slow time dimension. Discretize into , The dimension is The complex field matrix space, Let Q be the number of discrete sampling points within a PRT, and let Q be the total number of pulses in a CPI. The result of coherent accumulation is

[0073] (20)

[0074] in, Let be the discrete Fourier transform matrix, then we have , , is the discrete Fourier point number.

[0075] (twenty one)

[0076] Steps 3-5: Constant false alarm rate detection and spot aggregation

[0077] To reduce noise interference and enhance the distribution differences of false targets on different range-velocity planes, further analysis was conducted on the range-velocity two-dimensional plane. Perform constant false alarm rate detection and clustering to obtain

[0078] (twenty two)

[0079] in, For detection function

[0080] (twenty three)

[0081] This represents each data unit to be inspected in the range-velocity plane. For detection thresholds that depend on a specific false alarm rate detector, It is the modulus. This represents a clustering operator.

[0082] Then, extract all points with a value of 1 from the detection plane and record the corresponding distance and velocity coordinates.

[0083] (twenty four)

[0084] in, For the first The total number of candidate targets in each detection plane. and They represent the first In the detection plane, the first The distance and velocity coordinates of each target.

[0085] Steps 3-6: RVM

[0086] calculate and The Euclidean distance between all candidate target points is denoted as .

[0087] (25)

[0088] Then, iterate through the distances of all targets and perform distance-velocity matching on all targets in the detection plane, i.e., satisfy...

[0089] (26)

[0090] Then the detection plane is considered The Target and Detection Plane The The targets are the same, and the frequency of this target appearing in all detection planes is counted. , Threshold Due to the limitations of radar range and velocity resolution, the calculation is as follows:

[0091] (27)

[0092] in The sampling rate of the radar. and These are the range and velocity resolutions of the radar, respectively. Then, the M / N criterion is used to determine... In each detection plane, the threshold for the frequency of target occurrence is: Remove frequency of occurrence target point The process of obtaining the value is a known existing technology, and will not be described in detail here.

[0093] Steps 3-7: PV Analysis

[0094] After the above steps, most sidelobe false targets are eliminated, and the remaining candidate targets can be identified using peak differences. Assuming that after range-velocity matching, the range and velocity coordinates of the remaining candidate targets are...

[0095] (28)

[0096] Then according to the formula The classification results are used to extract the same target in different detection planes on the coherent accumulation plane. The value in is denoted as

[0097] (29)

[0098] express The Each element.

[0099] Calculate the peak variance of each target point

[0100] (30)

[0101] The target with the smallest peak variance is selected as the true target, and other targets are identified as false targets and removed from the detection plane.

[0102] Simulation verification and analysis

[0103] Simulation parameters:

[0104] The baseband signal is composed of five sub-pulses, each selected from a sequence of a completely complementary code set: (4,4,11)-CCCs, (4,4,13)-CCCs, (4,4,24)-CCCs, (4,4,5)-CCCs, and (4,4,7)-CCCs. Each sub-pulse has a bandwidth of [missing information]. The time widths are respectively , , , , Frequency offset respectively , , , , sampling frequency .

[0105] Considering a single-target scenario, the target distance is... The carrier frequency is The target speed is Signal-to-noise ratio Three types of intermittent sampling forwarding interference were set: ISDRJ, ISRRJ, and ISCRJ, with interference-to-noise ratios of [missing information]. , , .

[0106] Simulation analysis:

[0107] Appendix Figure 2 The time-domain waveform and spectrum of the designed non-uniform agile subpulse with variable parametric time-sensitive multiple subpulse width and frequency are shown in the appendix. Figure 3 This is a time-domain waveform diagram of the radar signal and the composite interference signal echo. (See attached diagram.) Figure 4 It can be seen that after frequency domain filtering, pulse compression, and peak compensation, the peak amplitude fluctuation of the real target is smaller in different detection planes, while the peak amplitude fluctuation of the false target is larger. Based on this characteristic, PV analysis can be performed in the second stage of processing to eliminate false targets. (Appendix) Figure 5 The target detection planar map, obtained after slow-time coherent accumulation, constant false alarm rate detection, and point clustering, reveals a large number of false targets. (See attached...) Figure 6 It can be seen that the first-stage RVM processing eliminated most of the false targets caused by residual sidebands and sidelobes of ISRJ. (See attached...) Figure 7 As can be seen, after the second stage of PV analysis, all false targets were successfully eliminated and real targets were detected. These results demonstrate the effectiveness of the present invention.

[0108] Those skilled in the art will recognize that the embodiments described herein are for the purpose of helping to understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Various modifications and variations can be made to the invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the invention should be included within the scope of the claims of the invention.

Claims

1. A method for handling main lobe multi-interference based on multi-sub-pulse variable parametric time-sensitive waveforms, characterized in that, include: S1. Utilizing the ideal correlation characteristics of CCCs, the transmit waveform with low sidelobes and favorable for separation of multiple pulse signals is designed as follows: ； In the formula, For the first The baseband signal waveform based on CCCs for each sub-pulse. For the first The pulse width of each sub-pulse Let Heaviside be the unit step function. For frequency hopping interval, It is a frequency hopping code sequence. , Indicates the number of sub-pulses; S2. Based on the different forwarding methods, establish a composite ISRJ model and add it to the radar echo signal; S3. Perform two-stage interference identification and elimination processing on the radar echo signal generated in step S2 based on RVMPV analysis.

2. The method for processing main lobe multi-interference based on multi-sub-pulse variable parametric time-sensitive waveforms according to claim 1, characterized in that, Step S2 is as follows: Depending on the forwarding method, ISRJ can be divided into direct intermittent sampling forwarding interference, repeated intermittent sampling forwarding interference, and cyclic intermittent sampling forwarding interference; Different ISRJs are uniformly represented as Then the first The ISRJ interference signal of each transmitted waveform is represented as follows: ； in, Indicates the distance delay caused by the target. Indicates the pulse repetition interval. This indicates the total number of interferences; Then the radar received the first The echo signal is ； in, Indicates the radar received the first The target echo generated by the transmitted waveform; This indicates that the mean is zero and the variance is... Complex Gaussian white noise.

3. The method for processing main lobe multi-interference based on multi-sub-pulse variable parametric time-sensitive waveforms according to claim 2, characterized in that, Step S3 specifically includes the following sub-steps: S31. Frequency domain filtering: Since the sub-pulses are separated in the frequency domain, the design... Each sub-pulse is blocked by a band-stop filter to obtain... Group filtering results; S32, pulse compression, for The filtered results are then subjected to pulse compression processing. S33. Peak Compensation: Due to the non-uniform variation of sub-pulse widths, the pulse compression gains corresponding to different sub-pulse combinations are not equal, resulting in unequal target peak values. Compensation is performed on the pulse compression peak points; it is known that... When all sub-pulses are compressed simultaneously, the output peak value is The sum of the peak values ​​of each sub-pulse, i.e. ； in, It is the bandwidth of the sub-pulse. For the first Pulse width, This represents the modulation amplitude of the nth sub-pulse, corresponding to the filtering out of the nth sub-pulse. After each sub-pulse, the loss of the pulse compression output peak value is: ； Compensating for the pulse compression result, we obtain: ； S34. Slow-time coherent accumulation: Since coherent accumulation does not change the difference between the peak values ​​of the real target and the false target, along... Coherent accumulation of the slow time dimension, Discretize into ,but The coherent accumulation result is: ； in, Let T be the discrete Fourier transform matrix, and let T denote the transpose operation. S35, constant false alarm rate detection and point aggregation, for distance-velocity two-dimensional plane By performing constant false alarm rate (CFAR) detection and clustering, we obtain: ； in, For the detection function, Represents clustering operators; S36, Calculation and The Euclidean distance between all candidate target points. Then, iterate through the distances of all targets and perform distance-velocity matching on all targets in the detection plane. S37. Based on the matching results of step S36, extract the same target in different detection planes in the coherent accumulation plane. The values ​​in the table are used to calculate the peak variance of each target point. The target with the smallest peak variance is taken as the real target, and the other targets are judged as false targets and removed from the detection plane.

4. The method for processing main lobe multi-interference based on multi-sub-pulse variable parametric time-sensitive waveforms according to claim 3, characterized in that, Step S36 involves performing range-velocity matching on targets across all detection planes, i.e., calculating the range-velocity. and The Euclidean distance between candidate target points is less than or equal to the threshold. Then it is believed and The two candidate target points in the data are the same target.

5. The method for processing main lobe multi-interference based on multi-sub-pulse variable parametric time-sensitive waveforms according to claim 4, characterized in that, threshold Due to the limitations of radar range and velocity resolution, the calculation formula is as follows: ； in, The sampling rate of the radar. and These are the radar's range and velocity resolutions, respectively. is the discrete Fourier point number.

6. The method for processing main lobe multi-interference based on multi-sub-pulse variable parametric time-sensitive waveforms according to claim 5, characterized in that, This also includes counting the frequency of each candidate target point obtained from step S36 across all detection planes, and eliminating those with a frequency less than or equal to a threshold. Candidate target points.

7. The method for processing main lobe multi-interference based on multi-sub-pulse variable parametric time-sensitive waveforms according to claim 6, characterized in that, The acquisition process is as follows: The interference system first uses a periodic sampling function The transmit waveform designed in step S1, characterized by low sidelobes and favorable for multi-pulse signal separation, undergoes interrupt sampling, followed by store-and-forward. The periodic sampling process can be represented as follows: ； in, , For window functions, The pulse width of the sampled signal. Indicates the repetition interval of the sampled signal. This represents a convolution operation, where s is the sampling pulse number. The number of sampling pulses, Represents the Kronecker function; Based on the expressions obtained for direct intermittent sampling forwarding interference, repeated intermittent sampling forwarding interference, and cyclic intermittent sampling forwarding interference, they are as follows: ； ； ； in, and These are the number of repeated forwardings within the sampling period in ISRRJ and ISCRJ, respectively.

Images