Jamming suppression method for linear frequency modulation continuous wave DRFM jamming

Abstract

The application discloses a jamming suppression method for a linear frequency modulation continuous wave (LFMCW) system DRFM jamming, which comprises the following steps: after a mixed signal of a target echo signal and a DRFM jamming signal is mixed with a local oscillator signal, a low-pass filter is used to obtain an interfered intermediate frequency signal; short-time Fourier transform is performed on the sampled intermediate frequency signal to obtain a time-frequency spectrum, an interfered position is found on the time-frequency spectrum, and a corresponding binary mask image is generated; and the binary mask is used to filter out the jamming. The application analyzes the jamming form of the spectrum dispersion jamming in the intermediate frequency under the LFMCW system, and according to the characteristics of the jamming form, short-time Fourier transform is used to convert the signal from the time domain to the time-frequency domain, and binary mask filtering is performed on the time-frequency domain to remove the jamming. The method can realize jamming suppression under the condition that the SMSP jamming slice number n is less than or equal to 10 and the jamming-to-signal ratio (JSR) is less than or equal to 40 dB.

Description

Technical Field

[0001] This invention relates to anti-interference technology for linear frequency modulation systems, specifically to an interference suppression method for DRFM interference in linear frequency modulation continuous wave systems. Background Technology

[0002] In recent years, the rapid development of digital chips, represented by FPGAs and DSPs, has made high-speed, high-precision digital signal processing easier to achieve, greatly reducing the difficulty of implementing linear frequency modulated continuous wave (LFM) short-range detection systems. Consequently, jamming techniques targeting LFM systems have also continuously evolved. In recent years, electronic warfare has developed rapidly, with various jamming methods emerging. Based on their action path and jamming effect, jamming of radio short-range detection systems (referring only to man-made active jamming) can be divided into two categories: energy-based jamming and information-based jamming. Information-based jamming, due to its portability, intelligence, and diverse jamming methods, has become the most significant and deadliest threat faced by radio short-range detection systems in the complex electromagnetic environment of the battlefield.

[0003] The latest fourth-generation jammers currently employ Digital Radio Frequency Memory (DRFM) technology for repeater-type jamming. DRFM technology can adapt to complex and variable electromagnetic environments, ensuring high coherence between the jamming signal and the transmitted signal, resulting in effective interference. Therefore, DRFM technology has been widely used in short-range detection countermeasures systems, and it is receiving increasing attention in these systems. Among current radio jamming techniques, multi-false-target jamming (SMSP and slice-and-reconstruction (C&I)) within DRFM technology offers the best and most efficient interference effect against linear frequency modulated continuous wave (LFM) short-range detection systems. Extensive anti-jamming research has been conducted by scholars both domestically and internationally on this type of interference. Currently, two main anti-jamming measures are being studied. The first is to improve the stealth of the detection system, such as by selecting the operating frequency outside the atmospheric transmission window; or by using frequency agility or frequency adaptive technology, which significantly reduces the interference effect when the jamming signal and the detection system's operating carrier frequency are inconsistent. Some researchers have proposed a dual-channel correlation detection method for variable modulation slope short-range detection systems to suppress DRFM interference. The second type is interference suppression based on signal processing. Compared to other anti-interference methods, signal processing is currently the most promising and widely researched area of ​​anti-interference. The countermeasures generally employ an "interference identification-interference removal" approach, with interference identification being the core technology, requiring the identification of a stable and discriminative feature. Commonly used countermeasures typically improve the output SJNR through filtering and accumulation methods, often combined with modern signal processing techniques such as time-frequency transform and wavelet transform, attempting to separate interference and signal in a certain dimension, thereby filtering out interference and extracting the signal. Some researchers have proposed a cross-correlation detection and decision method for short-range detection systems using linear frequency modulation (LFM) to combat intermittent relay interference and perform interference filtering. Summary of the Invention

[0004] The purpose of this invention is to provide an interference suppression method for DRFM interference in linear frequency modulated continuous wave systems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, the present invention provides an interference suppression method for Linear Frequency Modulated Continuous Wave (DRFM) interference, comprising:

[0006] The echo signal with interference is mixed and amplified with the transmitted signal by the receiver to obtain the intermediate frequency signal;

[0007] Use short-time Fourier transform to transform the intermediate frequency signal from the time domain to the time-frequency domain;

[0008] SMSP interference generates several identical but variable slope linear frequency modulation interference signals at the intermediate frequency. Based on this characteristic, the interference location is found in the time-frequency domain, and the corresponding binary mask image is generated.

[0009] Interference is filtered out using a binary mask, and the filtered time-frequency domain graph is weighted and averaged along the time axis to obtain the spectrum result after filtering out SMSP interference.

[0010] Secondly, the present invention also provides an interference suppression system for linear frequency modulated continuous wave (DRFM) interference, comprising:

[0011] The receiver mixer module is used to mix and amplify the echo signal with interference with the transmitted signal to obtain the intermediate frequency signal.

[0012] The short-time Fourier transform module uses short-time Fourier transform to transform the intermediate frequency signal from the time domain to the time-frequency domain;

[0013] The binary mask image module is used to find the interference location in the time-frequency domain and generate the corresponding binary mask image;

[0014] The interference filtering module uses a binary mask to filter out interference. It then performs a weighted average of the filtered time-frequency domain graph along the time axis to obtain the spectrum result after removing the SMSP interference.

[0015] Thirdly, this application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in the first aspect above.

[0016] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method described in the first aspect above.

[0017] Fifthly, this application also provides a computer program product, including a computer program, characterized in that the computer program, when executed by a processor, implements the method described in the first aspect above.

[0018] Compared with the prior art, the significant advantages of this invention are:

[0019] (1) Fast anti-interference response speed: The SMSP interference suppression method proposed in this invention, which combines time-frequency domain transformation and binary mask time-frequency filter, can be implemented on the platform of programmable devices such as FPGA. With the help of the platform, interference can be quickly identified and suppressed.

[0020] (2) Strong anti-interference adaptability: The algorithm proposed in this invention can not only identify and suppress spectrum dispersion interference, but also identify and suppress store-and-forward interference from other fourth-generation jammers. Attached Figure Description

[0021] Figure 1 This is a flowchart of the interference suppression method for DRFM interference in the linear frequency modulated continuous wave system according to the present invention.

[0022] Figure 2 This is a block diagram of a linear frequency modulated continuous wave short-range detector system.

[0023] Figure 3 This is a time-frequency diagram of the spectral dispersion interference signal.

[0024] Figure 4 This represents the intermediate frequency spectrum affected by SMSP interference.

[0025] Figure 5 This is a time-frequency diagram of the intermediate frequency signal affected by SMSP interference.

[0026] Figure 6 This is a flowchart of SMSP interference suppression based on binary mask time-frequency domain filtering.

[0027] Figure 7 A comparison chart showing the spectral performance after interference suppression. Detailed Implementation

[0028] like Figure 1 As shown, an interference suppression method for DRFM interference in linear frequency modulated continuous wave systems includes receiver mixing, short-time Fourier transform, binary mask image, and interference filtering. The specific steps are as follows:

[0029] Step 1: After the receiver mixes the received signal containing target echo, spectrum dispersion interference and noise, it enters the signal processing module and is sampled by the AD chip to obtain the intermediate frequency signal affected by SMSP interference.

[0030] Step 2: Following Step 1, the sampled digital signal enters the signal processing module, where the sampled intermediate frequency signal undergoes STFT transformation. The STFT calculation module includes a data sliding window module, a windowing module, and a DFT calculation module. The STFT calculation module performs an M-point data sliding window and N-point data truncation on the input AD data stream. Then, the windowing module reduces the impact of signal sidelobes and spectral leakage, increasing the frequency measurement dynamic range. Next, the DFT array calculates the spectrum of each N-point truncated data segment. Finally, the spectrum data calculated by the DFT array is stored in a FIFO array. Parameter N determines the frequency resolution, while parameter M determines the time resolution. When M decreases, the time resolution increases, but the computational load also increases, requiring more DFT calculation units in the DFT array. The STFT calculation framework requires a DFT calculation module capable of pipelined operation with limited resources.

[0031] Step 3: Based on Step 2, the time-frequency domain after obtaining the STFT is entered into the mask filtering interference suppression binary mask data calculation module. This module is mainly divided into a storage part and a calculation part. The storage part includes a FIFO1 array for buffering, a FIFO2 array for storing time-frequency domain data, and a FIFO3 array for storing binarized images. The calculation part includes a log calculation array, a histogram calculation module, and a binarization conversion module.

[0032] Step 4: After obtaining the binary mask data according to Step 3, the SMSP interference can be filtered out according to the filtering algorithm mentioned above. The mask filtering module includes a signal detection module and a spectrum accumulation module. The signal result calculated by the signal detection module, the binarized result output by FIFO3, and the spectrum power spectrum value are used to output the spectrum after filtering out the interference.

[0033] Furthermore, after the receiver mixes the received signal containing target echo, spectrum dispersion interference and noise, the SMSP interference produces dense spurious peaks at the intermediate frequency.

[0034] Let the complex envelope of the transmitted signal be...

[0035]

[0036] In the formula: T is the pulse width; k is the modulation slope;

[0037] Based on the SMSP interference generation mechanism, a single subpulse is...

[0038]

[0039] Among them: A J The amplitude of the interference is k; k' is the frequency modulation slope of the interference, k' = nk. By replicating the sub-pulse n times, the time-domain waveform of the SMSP interference is obtained as follows:

[0040]

[0041] SMSP interference is formed by the compression and duplication of the transmitter's transmitted signal, and is a linear transformation of the transmitted signal. The interference signal has a similar structure to the transmitter's transmitted signal. After mixing, it produces a near-passband linear frequency modulated signal, which causes it to generate a series of comb-like dense false targets on the echo intermediate frequency. Therefore, when the interference power is very high, SMSP interference not only has a deceptive interference effect, but also a suppressive interference effect, affecting the detection and tracking performance of the detection system. Since the signal received by the RF front end is small and contains high-frequency noise, it is amplified and filtered to obtain an in-band intermediate frequency signal, which then enters the signal processing module.

[0042] Furthermore, a short-time Fourier transform is performed on the discrete intermediate frequency signal to transform the signal from the time domain to the time-frequency domain. The expression for this transformation is:

[0043]

[0044] Where w(t) is a short-time window function, its characteristics are:

[0045] The target signal in step 2 is represented as a straight line with a single frequency distributed along the time axis. A series of dense comb-shaped false targets are represented in the time-frequency domain as multiple broadband linear frequency modulation interferences with a certain slope on the frequency axis. This slope is determined by the number of SMSP interference slices n set by the DRFM jammer, that is, the number of interference sub-pulses n set in the previous step. When n is larger and the interference power is greater, the interference will be more dense.

[0046] Furthermore, since the SMSP interference and the target echo overlap in multiple dimensions such as time, frequency, and energy, the SMSP interference generates several identical but variable slope linear frequency modulation interference signals in the intermediate frequency. Based on this characteristic, the interference location is found in the time-frequency domain, and the corresponding binary mask image is generated.

[0047] Furthermore, the sampled digital signal enters the signal processing module, which performs STFT transformation on the sampled intermediate frequency signal. The STFT calculation module includes a data sliding window module, a windowing module, and a DFT calculation module. The STFT calculation module performs M-point data sliding windowing and N-point data truncation on the input AD data stream. Then, the windowing module reduces the influence of signal sidelobes and spectral leakage, and increases the frequency measurement dynamic range. Then, the DFT array calculates the partial spectrum of the N-point truncated data. Finally, the spectrum data calculated by the DFT array is stored in the FIFO array.

[0048] Furthermore, to address the interference patterns generated by SMSP at the intermediate frequency, a corresponding binary mask image is generated using time-frequency domain data. First, a minimum signal-to-noise ratio (SNR) value is set on the spectrum to allow the detection of the signal. req According to SNR req The size divides the time-frequency domain into two parts: one is the noise region, and the other is the target signal plus interference region, thus obtaining the binary mask image.

[0049] Furthermore, the original time-frequency domain matrix is ​​filtered using a binary mask image. All regions where the binary mask is 1 are replaced with the values ​​of the noise region, while regions where it is 0 remain unchanged. The filtered time-frequency domain image is then weighted and averaged along the time axis to obtain the spectrum after removing the SMSP interference.

[0050] The present invention also provides an interference suppression system for DRFM interference in linear frequency modulated continuous wave systems, comprising:

[0051] The receiver mixer module is used to mix and amplify the echo signal with interference with the transmitted signal to obtain the intermediate frequency signal.

[0052] The short-time Fourier transform module, which is part of the signal processing module, uses short-time Fourier transform to transform the intermediate frequency signal from the time domain to the time-frequency domain.

[0053] The binary mask image module, which is part of the signal processing module, is used to find the interference location in the time and frequency domain and generate the corresponding binary mask image because the SMSP interference and the target echo overlap in multiple dimensions such as time, frequency, and energy. This is manifested in the fact that the SMSP interference generates several identical but variable slope linear frequency modulation interference signals in the intermediate frequency.

[0054] The interference filtering module, which is part of the signal processing module, filters out interference using a binary mask. It then performs a weighted average of the filtered time-frequency domain graph along the time axis to obtain the spectrum result after removing the SMSP interference.

[0055] The specific implementation methods of each module of the above system are the same as those of the aforementioned interference suppression methods, and will not be repeated here.

[0056] The present invention will be further described below with reference to the embodiments.

[0057] Figure 1Presenting a block diagram of a linear frequency modulation (LFM) short-range detection system: A radio short-range detection system utilizes electromagnetic wave environmental information to sense targets. This invention primarily studies interference suppression in a continuous wave short-range detection system using a LFM system. This system possesses advantages such as high range resolution, no range blind zone, strong anti-interception capability, and simple structure, making it well-suited for short-range detection. This invention proposes a method for suppressing SMSP interference using a combination of time-frequency domain transform and a binary mask time-frequency filter. After mixing the target echo signal and the SMSP interference signal with the local oscillator signal, a low-pass filter is applied to obtain the interfered intermediate frequency (IF) signal. The sampled IF signal undergoes a short-time Fourier transform to obtain the time spectrum. The location of the interference is located on the time spectrum, and a corresponding binary mask image is generated. Interference is filtered out using the binary mask.

[0058] Figure 2 The time-frequency diagram of the RF signal of SMSP interference is shown. The frequency segmentation characteristics of SMSP interference can be clearly seen. Each segment has the same bandwidth as the transmitter's transmitted signal, but the slope is n times that of the transmitted signal.

[0059] The present invention implements the following steps to address this interference signal:

[0060] Step 1: First, the sampled intermediate frequency (IF) signal undergoes STFT transformation. The STFT calculation module includes a data sliding window module, a windowing module, and a DFT calculation module. The STFT calculation module performs an M-point data sliding window and N-point data truncation on the input AD data stream. Then, the windowing module reduces the impact of signal sidelobes and spectral leakage, increasing the frequency measurement dynamic range. Next, the DFT array calculates the partial spectrum of the N-point truncated data. Finally, the spectrum data calculated by the DFT array is stored in a FIFO array. Parameter N determines the frequency resolution, while parameter M determines the time resolution. When M decreases, the time resolution increases, but the computational load also increases, requiring more DFT calculation units in the DFT array. The STFT calculation framework needs a DFT calculation module capable of pipelined operation with minimal resources. This invention uses a partially computed DFT algorithm for STFT framework calculation, meeting algorithm performance requirements while reducing resource usage. The DFT calculation mainly involves the multiplication and accumulation of the intermediate frequency signal value and the rotation factor. Therefore, the DFT algorithm of this invention is mainly divided into three modules: the rotation factor reading module, the input reading module, and the DFT PE (processing element) Region. The DFT algorithm is a mature existing method. This invention implements the algorithm on the FPGA platform using the Verilog language.

[0061] Step 2: After obtaining the time-frequency domain data after STFT, the process proceeds to the mask filtering interference suppression binary mask data calculation module. This module is mainly divided into a storage section and a calculation section. The storage section includes a FIFO1 array for buffering, a FIFO2 array for storing time-frequency domain data, and a FIFO3 array for storing the binarized image. The calculation section includes a log calculation array, a histogram calculation module, and a binarization conversion module. The histogram module consists of a two-dimensional storage matrix, where the number of storage cells represents the number of spectral power spectrum values ​​counted by the histogram in this paper, and the bit width of a single storage cell represents the number of a single category. The binarization conversion module uses the threshold value calculated previously received, binarizes the value output from FIFO2, and concatenates the original stored spectral power spectrum value with the binarized result before storing it in FIFO3.

[0062] Step 3: After obtaining the binary mask data, the SMSP interference can be filtered out according to the filtering algorithm described above. The mask filtering module includes a signal detection module and a spectrum accumulation module. The signal result calculated by the signal detection module, the binarized result output by FIFO3, and the spectrum power value are used to output the spectrum after filtering out the interference.

[0063] Step 4: After obtaining the spectrum with interference filtered out, the target signal spectral lines are clearly detected compared to the spectrum before interference filtering. Testing shows that the hardware implementation of this interference suppression algorithm can adapt to interference inputs under typical parameters and demonstrates interference suppression effectiveness.

[0064] Assume the number of interfering sub-pulses n is 5, that is, the number of SMSP interference slices is 5, and the interference-to-signal ratio is 35dB.

[0065] Figure 3 The time-frequency diagrams of the fuze transmission signal and the SMSP interference signal are shown. The frequency segmentation characteristics of the SMSP interference can be clearly seen. Each segment has the same bandwidth as the fuze transmission signal, but the slope is n times that of the transmission signal.

[0066] Figure 4 The intermediate frequency (IF) spectrum of the signal interfered with by SMSP is presented. At the fuze receiver, the received signal containing the target echo, spectral dispersion interference, and noise is mixed to obtain the interfered IF spectrum. The graph shows that the SMSP interference generates dense false peaks at the IF, while the real target appears as a single peak. The graph also shows the IF spectra corresponding to interference-to-signal ratios (ISRs) of 27 dB and 35 dB. When the ISR is 35 dB, the false peaks generated by the SMSP interference will overwhelm the spikes in the target signal, creating a suppressive interference effect.

[0067] Figure 5The time-frequency diagram of the intermediate frequency signal interfered with by SMSP is shown. It can be seen from the time-frequency diagram that the target signal is a straight line with a single frequency distributed along the time axis, while the SMSP interference produces multiple broadband linear frequency modulation interferences with a certain slope on the time axis. In fact, the number of slices n of this slope SMSP interference will become more and more dense as n increases and the interference power increases.

[0068] Figure 6 A flowchart of SMSP interference suppression based on binary mask time-frequency domain filtering is presented. After a series of processing steps on the digital signal after the echo intermediate frequency sampling, SMSP interference can be effectively removed.

[0069] Figure 7 A comparison of the spectral performance after interference suppression is presented. The figure shows that when the number of interfering sub-pulses n is 5 (i.e., the number of SMSP interference slices is 5) and the interference-to-signal ratio is 35dB, the interference signal completely overwhelms the target signal. The combined time-frequency domain transformation and binary mask time-frequency filter for SMSP interference suppression can effectively suppress SMSP interference and identify the target signal.

Claims

1. An interference suppression method for Linear Frequency Modulated Continuous Wave (DRFM) interference, characterized in that, Includes the following steps: The echo signal with interference is mixed and amplified with the transmitted signal by the receiver to obtain the intermediate frequency signal; Use short-time Fourier transform to transform the intermediate frequency signal from the time domain to the time-frequency domain; SMSP interference generates several identical but variable-slope linear frequency modulated interference signals at the intermediate frequency (IF). Based on this characteristic, the interference location is located in the time-frequency domain, and a corresponding binary mask image is generated. To generate the corresponding binary mask image using time-frequency domain data, a minimum signal-to-noise ratio (SNR) value is first set in the spectrum to allow the detection of the signal. req According to SNR req The size divides the time-frequency domain into two parts: one is the noise region, and the other part is the target signal plus interference region, thus obtaining the binary mask image; Interference is filtered out using a binary mask, and the filtered time-frequency domain graph is weighted and averaged along the time axis to obtain the spectrum result after filtering out SMSP interference.

2. The method according to claim 1, characterized in that, After the receiver mixes the received signal containing target echo, spectrum dispersion interference and noise, the SMSP interference produces a false peak at the intermediate frequency. Let the complex envelope of the transmitted signal be... In the formula: T is the pulse width; k is the modulation slope; Based on the SMSP interference generation mechanism, a single subpulse is... Among them, A J The amplitude of the interference is k; k' is the frequency modulation slope of the interference, k' = nk. By replicating the sub-pulse n times, the time-domain waveform of the SMSP interference is obtained as follows:

3. The method according to claim 2, characterized in that, Performing a short-time Fourier transform on a discrete intermediate frequency signal transforms the signal from the time domain to the time-frequency domain. The expression for this transformation is: Where w(t) is a short-time window function, its characteristics are:

4. The method according to claim 3, characterized in that, The sampled digital signal enters the signal processing module, which performs STFT transformation on the sampled intermediate frequency signal. The STFT calculation module includes a data sliding window module, a windowing module, and a DFT calculation module. The STFT calculation module performs M-point data sliding windowing and N-point data truncation on the input AD data stream. Then, the windowing module increases the frequency measurement dynamic range. Finally, the DFT array calculates the partial spectrum of the N-point truncated data. The spectrum data calculated by the DFT array is stored in the FIFO array.

5. The method according to claim 1, characterized in that, The original time-frequency domain matrix is ​​filtered by a binary mask image. All regions where the binary mask is 1 are replaced with the values ​​of the noise region, while the regions where it is 0 remain unchanged. The filtered time-frequency domain image is then weighted and averaged along the time axis to obtain the spectrum after removing the SMSP interference.

6. An interference suppression system for Linear Frequency Modulated Continuous Wave (DRFM) interference, characterized in that, The system for implementing the method according to any one of claims 1 to 5 comprises: The receiver mixer module is used to mix and amplify the echo signal with interference with the transmitted signal to obtain the intermediate frequency signal. The short-time Fourier transform module uses short-time Fourier transform to transform the intermediate frequency signal from the time domain to the time-frequency domain; The binary mask image module is used to find the interference location in the time-frequency domain and generate the corresponding binary mask image; The interference filtering module uses a binary mask to filter out interference. It then performs a weighted average of the filtered time-frequency domain graph along the time axis to obtain the spectrum result after filtering out the SMSP interference.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method described in any one of claims 1-5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method described in any one of claims 1-5.

Images