A radar anti-complex modulation intermittent sampling and forwarding interference method

Abstract

A radar anti-interference method for complex modulation intermittent sampling forwarding interference is proposed. Based on wavelet transform, the interference echo modulus is denoised. The maximum and minimum values ​​of the denoised echo modulus are extracted, and a threshold is calculated using weighted averages. The time-domain location of the interference in the echo is obtained through binarization comparison. Narrow pulse rejection is performed on the interference location to eliminate the interference signal. For the target echo after narrow pulse rejection, a sparse recovery regularization model is established based on the sparse frequency domain characteristics of the de-skewing signal. The regularization sparse recovery model is solved using convex optimization to obtain the target echo spectrum. The target time-domain echo is obtained through inverse Fourier transform and inverse de-skewing. Matched filtering pulse compression yields the radar detection result resistant to complex modulation intermittent sampling forwarding interference. This invention can effectively suppress complex intermittent sampling forwarding interference and achieve target detection without relying on the complex modulation parameters of intermittent sampling forwarding interference, and has stronger universality compared to traditional methods.

Description

Technical Field

[0001] This invention belongs to the field of radar electronic countermeasures technology, and specifically relates to a method for radar to resist complex modulation intermittent sampling and forwarding interference. Background Technology

[0002] With the rapid development of radio frequency store-and-forward technology, radar jamming has achieved a technological leap from non-coherent to coherent. Intermittent sampling-and-forward jamming, through low-rate sampling and forwarding, solves the problem of jammer transmission-receiver isolation and can produce a dense decoy jamming effect on radar. Addressing the issue of the strong regularity of decoy targets in intermittent sampling-and-forward jamming, complex modulation intermittent sampling-and-forward jamming has become a research hotspot, such as segmented frequency shifting and agile noise composite modulation intermittent sampling-and-forward jamming. Complex modulation intermittent sampling-and-forward jamming can reduce the regularity of jammed targets and produce jamming effects that combine deception and suppression, posing one of the main threats to current radar detection.

[0003] Intermittent sampling-forwarding interference suppression methods include receiver signal processing and transmitter waveform design. Based on prior knowledge of the radar's transmitted linear frequency modulated waveform, receiver signal processing employs band-stop filtering and time-frequency filtering. The band-stop filtering method designs a frequency-domain band-stop filter to create a notch in the interference signal's spectrum. The time-frequency filtering method first de-skews the signal and then designs a two-dimensional time-frequency filter based on the time-frequency energy distribution. Under the prior information of the intermittent sampling-forwarding interference repetition period and duty cycle, the interference energy and false target gain can be suppressed by designing the transmitter's waveform and unmatched filter. However, complex modulation reduces the regularity of intermittent sampling-forwarding interference energy in the frequency and time-frequency domains, leading to a deterioration in the performance of the aforementioned interference suppression methods. Furthermore, complex modulation makes the extraction of prior parameters for intermittent sampling-forwarding interference more complex, which also degrades the performance of waveform-based interference suppression methods.

[0004] To address the issue of reduced performance in suppressing intermittent sampling and forwarding interference caused by complex modulation, this invention proposes a method for resisting complex modulation intermittent sampling and forwarding interference. This method obtains the time-domain location of the interference signal by performing time-domain noise reduction on the interfered signal, then removes the echo of the interfered segment using a narrow pulse elimination method, obtains the complete echo signal through deskewing and sparse recovery processing, and finally obtains the anti-interference target detection result. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a radar anti-intermittent sampling forwarding interference method that can suppress intermittent sampling forwarding interference of segmented frequency shift and smart noise composite modulation, and accurately obtain target detection results without estimating complex modulation parameters. It can also resist slice forwarding and intermittent sampling forwarding interference, and has strong versatility. It has broad application prospects in the fields of air defense radar, anti-missile radar, guidance radar, airborne fire control radar and inverse synthetic aperture radar.

[0006] To achieve the above objectives, this invention employs the following technical solution: a radar anti-complex modulation intermittent sampling and forwarding interference method, comprising: s1. acquiring radar orthogonal demodulated echo data, using wavelet transform to reduce echo modulus noise, calculating the maximum and minimum echo modulus values ​​after noise reduction, and weighting a threshold based on these values, extracting sequences with threshold values ​​greater than the threshold as the corresponding time-domain positions of the intermittent sampling and forwarding interference signal; s2. performing time-domain narrow pulse elimination sequences based on the time-domain positions of the intermittent sampling and forwarding interference, and performing narrow pulse elimination processing on the IQ echoes at the corresponding positions; s3. establishing a regularized sparse recovery model based on the sparsity characteristics of the frequency domain signal; after de-skewing, the target signal is similar to an impulse response in the frequency domain, exhibiting sparsity characteristics in the frequency domain, establishing its sparse recovery model in the frequency domain, and obtaining the complete target echo signal spectrum by solving the regularized sparse recovery model; s4. obtaining the time-domain de-skewing target echo signal through inverse Fourier transform, and obtaining the time-domain target echo signal after inverse de-skewing processing; s5. obtaining the anti-interference detection result through matched filtering pulse compression processing.

[0007] Furthermore, the specific steps of s1 are as follows:

[0008] Given that the discrete sampled target echo is s, and the complex modulated intermittent sampling and forwarding interference signal is s... j If the noise signal is n, then the interfered radar echo is:

[0009] r = s + s j +n (1)

[0010] For the acquired interference echo data modulus |r|, wavelet transform is used for noise reduction to obtain smooth time-domain echo envelope data |r1|.

[0011] For the denoised time-domain echo envelope data, the maximum and minimum values ​​are calculated as max|r1| and min|r1|, respectively. Based on these values, the threshold ξ is calculated as follows:

[0012]

[0013] Where κ is a threshold weighting factor, which is required to ensure that the threshold can extract the disturbed signal as completely as possible. Based on this, the following steps are performed to obtain the disturbance time-domain location indication vector p:

[0014]

[0015] Furthermore, in s2,

[0016] For the interfered echo data, narrow pulse rejection processing is performed to obtain the echo data r2 after narrow pulse rejection;

[0017]

[0018] Furthermore, in s3,

[0019] For the data after narrow pulse processing, deskewing is performed to obtain the deskewing signal r3; assuming the deskewing signal is x, then the deskewing signal is...

[0020] r3=r2⊙x (5)

[0021] Where ⊙ represents the dot product;

[0022] For the frequency domain interference-free data after deskewing, a regularized sparse recovery model is established, and the complete interference-free frequency domain signal is obtained by solving the regularized model.

[0023] A diagonalized matrix is ​​constructed based on vector 1-p, and the matrix P is obtained by removing all rows of zero in this matrix. Since the undisturbed signal segment is the same before and after narrow pulse removal, we get:

[0024] Pr3=Ps1 (6)

[0025] In the above formula, s1=s⊙x represents the deskewing process performed on the echo of the target signal to be recovered. Theoretically, after deskewing, the target signal is an impulse function in the frequency domain, exhibiting frequency domain sparsity. Therefore, a sparse recovery regularization model is established, assuming the spectrum of the reconstructed signal is S1, and F represents the discrete Fourier transform matrix, where:

[0026]

[0027] Equation (6) can be expressed as:

[0028] Pr3 = PF -1 S1 (8)

[0029] Let y = Pr3, which is the target echo compressed data after narrow pulse removal and deskewing. Then the sparse recovery model is expressed as:

[0030]

[0031] st PF -1 S1=y (9)

[0032] Since the objective function and constraints can both be converted into real functions of the real and imaginary parts of the variables, and the first norm is a convex function, and the linear constraints are also a convex set, the objective echo spectrum obtained by sparse recovery is directly solved by the convex optimization method using formula (9):

[0033]

[0034] Furthermore, the specific steps of s4 are as follows:

[0035] The target echo spectrum data obtained by sparse recovery is processed by inverse Fourier transform to obtain de-skewed target echo data.

[0036]

[0037] The beneficial effects of this invention are as follows: This invention extracts the time-domain location of the interference signal by binarizing and comparing the echo model after noise reduction, and uses a narrow pulse rejection method to remove the interfered echo segments. This eliminates the need to rely on complex modulation parameters of the intermittent sampling and forwarding interference signal, thus exhibiting greater universality. By deskewing the echo after interference removal and using a sparse recovery method to obtain the complete echo signal, this invention solves the problems of gain loss and excessive false alarms in radar detection signal processing caused by narrow pulse rejection. This invention can suppress intermittent sampling and forwarding interference from segmented frequency shifting and smart noise composite modulation, and accurately obtain target detection results. Since this invention does not require estimation of complex modulation parameters, it can also counteract slice forwarding and intermittent sampling and forwarding interference, exhibiting strong universality and broad application prospects in fields such as air defense radar, anti-missile radar, guidance radar, airborne fire control radar, and inverse synthetic aperture radar. Areas not detailed in this invention are existing commonly used technologies. Attached Figure Description

[0038] The present invention will be further described below with reference to the accompanying drawings:

[0039] Figure 1 Flowchart for resisting complex modulation intermittent sampling and forwarding interference;

[0040] Figure 2 The echo amplitude is affected by intermittent sampling and forwarding interference modulated by clever noise.

[0041] Figure 3 It utilizes wavelet transform for noise reduction, along with the time-domain echo envelope and narrow pulse rejection threshold.

[0042] Figure 4 The time-domain result of the echo after narrow pulse rejection;

[0043] Figure 5 The pulse compression results are shown before and after interference suppression in the intermittent sampling and forwarding mode of smart noise modulation.

[0044] Figure 6 The pulse compression results are shown before and after intermittent sampling and forwarding interference suppression in segmented frequency shift modulation.

[0045] Figure 7 The results show the pulse compression before and after intermittent sampling and forwarding interference suppression. Detailed Implementation

[0046] The present invention will be further described in detail below with reference to embodiments and specific implementation methods:

[0047] Example 1

[0048] In the simulation experiment, the radar transmits a pulse signal with a duration of 32µs, a bandwidth of 8MHz, and a pulse repetition period of 1ms. The jammer's intermittent sampling period is 8µs, with a duty cycle of 25%. The jammer types are smart noise modulation intermittent sampling and forwarding jamming, segmented frequency shift intermittent sampling and forwarding jamming, and intermittent sampling and forwarding jamming. In the scenario, one target is set at a distance of 1.5km from the reference position, with a signal-to-noise ratio of 10dB and an interference-to-signal ratio of 15dB. This invention is used to perform interference suppression and target echo recovery under the above parameter settings.

[0049] like Figure 1 As shown, a radar anti-complex modulation intermittent sampling and forwarding interference method is proposed, where the discrete sampling target echo is s and the complex modulation intermittent sampling and forwarding interference signal is s. j If the noise signal is n, then the interfered radar echo is:

[0050] r = s + s j +n (1);

[0051] Step 1: For the acquired modulus value |r| of the distorted echo data, use wavelet transform to perform noise reduction processing to obtain smooth time-domain echo envelope data |r1|.

[0052] Step 2: For the denoised time-domain echo envelope data, calculate the maximum and minimum values ​​as max|r1| and min|r1|, respectively. Based on these values, calculate the threshold ξ as follows:

[0053]

[0054] Where κ is a threshold weighting factor, which is required to ensure that the threshold can extract the disturbed signal as completely as possible. Based on this, the following steps are performed to obtain the disturbance time-domain location indication vector p:

[0055]

[0056] For the interfered echo data, narrow pulse rejection processing is performed to obtain the echo data r2 after narrow pulse rejection.

[0057]

[0058] Step 3: Perform de-chewing on the data after narrow pulse processing to obtain the de-chewing signal r3. Assuming the de-chewing signal is x, then the de-chewing signal is...

[0059] r3=r2⊙x (5)

[0060] Where ⊙ represents the dot product.

[0061] Step 4: For the frequency domain interference-free data after deskewing, establish a regularized sparse recovery model, and obtain the complete interference-free frequency domain signal by solving the regularized model.

[0062] A diagonalized matrix is ​​constructed based on vector 1-p, and the matrix P is obtained by removing all rows of zero in this matrix. Since the undisturbed signal segment is the same before and after narrow pulse removal, we get:

[0063] Pr3=Ps1 (6)

[0064] In the above formula, s1=s⊙x represents the deskewing process performed on the echo of the target signal to be recovered. Theoretically, after deskewing, the target signal is an impulse function in the frequency domain, exhibiting frequency domain sparsity. Therefore, a sparse recovery regularization model is established, assuming the spectrum of the reconstructed signal is S1, and F represents the discrete Fourier transform matrix, where:

[0065]

[0066] Equation (6) can be expressed as:

[0067] Pr3 = PF -1 S1 (8)

[0068] Let y = Pr3, which is the target echo compressed data after narrow pulse removal and deskewing. Then the sparse recovery model is expressed as:

[0069]

[0070] st PF -1 S1=y (9)

[0071] Since the objective function and constraints can both be converted into real functions of the real and imaginary parts of the variables, and the first norm is a convex function, and the linear constraints are also a convex set, the objective echo spectrum obtained by sparse recovery is directly solved by the convex optimization method using formula (9):

[0072]

[0073] Step 5: Perform inverse Fourier transform processing on the target echo spectrum data obtained from sparse recovery to obtain de-skewed target echo data.

[0074]

[0075] Step 6: For the target echo that has undergone deslant processing, perform inverse deslant processing and execute matched filter pulse compression processing to obtain the detection result.

[0076] Figure 2 The figure shows the echo amplitude of the intermittent sampling and forwarding interference caused by smart noise modulation. The smart noise duration is 2µs and the noise template is white noise. As can be seen from the figure, the amplitude of the interference signal is significantly higher than that of the target echo and is a periodic pulse signal. Due to the influence of white noise modulation, the amplitude of a single interference slice is 4µs, and the amplitude envelope fluctuates greatly.

[0077] Figure 3 It utilizes the time-domain echo envelope after wavelet transform denoising and the narrow pulse rejection threshold, where the weighting factor is set to 0.25.

[0078] Figure 4 The image shows the time-domain result of the echo after narrow pulse removal. The smart noise modulation intermittent sampling and forwarding interference signal with an amplitude higher than the threshold in the echo has been removed, and the target echo at the corresponding position has also been removed.

[0079] Figure 5 The pulse compression results before and after interference suppression for smart noise modulation intermittent sampling and forwarding are shown. Figure 5 (a) shows the pulse compression result before interference suppression, which includes dense false targets, while the real target is completely suppressed, thus making it impossible to obtain the real target signal. Figure 5 (b) To obtain the pulse compression result after suppressing the smart noise modulation intermittent sampling forwarding interference using the present invention, the target located at a position of 1.5km can be obtained. The results show that the present invention can suppress the smart noise modulation intermittent sampling forwarding interference and recover the target signal.

[0080] Figure 6 The output shows the pulse compression results before and after interference suppression using segmented frequency shift modulation and intermittent sampling and forwarding. The radar signal is divided into four segments, each 8µs in length, with frequency shifts of 300kHz, 0kHz, -200kHz, and 100kHz, respectively. Figure 6 (a) shows the pulse compression result before interference suppression; the real target is completely suppressed. Figure 6 (b) To obtain the target located at 1.5 km using the pulse compression result after interference suppression, the results show that the present invention can suppress the segmented frequency shift modulation intermittent sampling and forwarding interference and recover the target signal.

[0081] Figure 7 The results are pulse compression before and after intermittent sampling and forwarding interference suppression. Figure 7 (a) is the pulse compression result before interference suppression, which contains dense false targets and makes it impossible to obtain the real target signal. Figure 7 (b) To obtain the target located at 1.5 km by utilizing the pulse compression result after interference suppression, the results show that the present invention can suppress intermittent sampling and forwarding interference and recover the target signal.

[0082] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A radar anti-complex modulation intermittent sampling and forwarding interference method, characterized in that: s1. Acquire radar orthogonal demodulated echo data, use wavelet transform to reduce echo modulus noise, calculate the maximum and minimum echo modulus values ​​after noise reduction, and calculate a threshold based on these maximum and minimum values. Extract sequences greater than the threshold as the corresponding time domain positions of intermittent sampling and forwarding interference signals. s2. Generate a narrow pulse rejection sequence based on the time-domain location of intermittent sampling and forwarding interference, and perform narrow pulse rejection processing on the IQ echoes at the corresponding locations; s3. Establish a regularized sparse recovery model based on the sparsity characteristics of the frequency domain signal; after de-skewing, the target signal is similar to an impulse response in the frequency domain, and it has sparse characteristics in the frequency domain. Establish its sparse recovery model in the frequency domain, and obtain the complete target echo signal spectrum by solving the regularized sparse recovery model; s4. Obtain the time-domain de-skewing target echo signal through inverse Fourier transform, and obtain the time-domain target echo signal after inverse de-skewing processing; s5. Obtain the anti-interference detection result through matched filtering pulse compression processing.

2. The radar anti-complex modulation intermittent sampling and forwarding interference method according to claim 1, characterized in that: The specific steps of s1 are as follows: Given that the discrete sampled target echo is s, and the complex modulated intermittent sampling and forwarding interference signal is s... j If the noise signal is n, then the interfered radar echo is: r=s+s j +n (1) For the acquired interference echo data modulus |r|, wavelet transform is used for noise reduction to obtain smooth time-domain echo envelope data |r1|. For the denoised time-domain echo envelope data, the maximum and minimum values ​​are calculated as max|r1| and min|r1|, respectively. Based on these values, the threshold ξ is calculated as follows: Where κ is a threshold weighting factor, which is required to ensure that the threshold can extract the disturbed signal as completely as possible. Based on this, the following steps are performed to obtain the disturbance time-domain location indication vector p:

3. The radar anti-complex modulation intermittent sampling and forwarding interference method according to claim 2, characterized in that: In s2, For the interfered echo data, narrow pulse rejection processing is performed to obtain the echo data r2 after narrow pulse rejection; 4. The radar anti-complex modulation intermittent sampling and forwarding interference method according to claim 3, characterized in that: In s3, For the data after narrow pulse processing, deskewing is performed to obtain the deskewing signal r3; assuming the deskewing signal is x, then the deskewing signal is... r3＝r2⊙x (5) Where ⊙ represents the dot product; For the frequency domain interference-free data after deskewing, a regularized sparse recovery model is established, and the complete interference-free frequency domain signal is obtained by solving the regularized model. A diagonalized matrix is ​​constructed based on vector 1-p, and the matrix P is obtained by removing all rows of zero in this matrix. Since the undisturbed signal segment is the same before and after narrow pulse removal, we get: Pr3=Ps1 (6) In the above formula, s1=s⊙x represents the deskewing process performed on the echo of the target signal to be recovered. Theoretically, after deskewing, the target signal is an impulse function in the frequency domain, exhibiting frequency domain sparsity. Therefore, a sparse recovery regularization model is established, assuming the spectrum of the reconstructed signal is S1, and F represents the discrete Fourier transform matrix, where: Equation (6) can be expressed as: Pr3=PF -1 S1 (8) Let y = Pr3, which is the target echo compressed data after narrow pulse removal and deskewing. Then the sparse recovery model is expressed as: Since the objective function and constraints can both be converted into real functions of the real and imaginary parts of the variables, and the first norm is a convex function, and the linear constraints are also a convex set, the objective echo spectrum obtained by sparse recovery is directly solved by the convex optimization method using formula (9):

5. A radar anti-complex modulation intermittent sampling and forwarding interference method according to claim 4, characterized in that: In s4, The target echo spectrum data obtained by sparse recovery is processed by inverse Fourier transform to obtain de-skewed target echo data.

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