Space-frequency adaptive anti-interference improvement processing method and device and storage medium

Abstract

The invention discloses a space-frequency adaptive anti-interference improvement processing method and device and a storage medium, and the method comprises the steps: obtaining a plurality of paths of analog intermediate frequency signals of an array antenna after down-conversion through a radio frequency module, and carrying out the preprocessing operation, thereby obtaining digital baseband data; converting the interference signal into frequency domain data through fast Fourier transform, and identifying the type of the interference signal through an intelligent detection technology; calculating a covariance matrix according to the frequency domain data, optimizing the covariance matrix based on the current interference signal type, and calculating a space-frequency adaptive weight according to the optimized covariance matrix; adaptive filtering is carried out on the frequency domain data after delay processing, and interference signals are suppressed; and obtaining time domain data after interference suppression through frequency spectrum optimization and inverse Fourier transform. According to the method, the anti-interference performance is remarkably improved, the calculation complexity is reduced, it is ensured that efficient and stable signal quality can still be maintained in a complex electromagnetic environment, and the method has a good practical application prospect.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of satellite navigation anti-jamming technology, and in particular to a space-frequency adaptive anti-jamming improved processing method and device and a storage medium. BACKGROUND

[0002] With the rapid development of satellite navigation technology, navigation systems are increasingly widely used in military, transportation, communication and other fields. Navigation signals are extremely susceptible to various electromagnetic interferences, including hostile active interference and non-hostile environmental noise, due to long transmission distance and weak power, resulting in decreased navigation accuracy and system reliability. Therefore, how to effectively suppress interference and ensure stable operation of the navigation system has become a key technical problem to be solved, which has promoted in-depth research on navigation terminal anti-jamming technology.

[0003] The current mainstream space-frequency adaptive processing (SFAP) technology divides each wideband signal received by an array antenna into a plurality of frequency spectrum components using fast Fourier transform, performs spatial domain adaptive filtering on each frequency spectrum component, combines spatial domain and frequency domain information to effectively suppress interference signals, improves the degree of freedom without increasing the dimension of the covariance matrix, and reduces the matrix operation complexity. However, these technologies are general processing methods and cannot dynamically identify and optimize different types of interference signal characteristics, resulting in limitations in anti-jamming performance in complex and variable interference environments, and cannot guarantee stable and excellent results.

[0004] Therefore, it is urgent to propose a space-frequency adaptive anti-jamming improved processing method that can improve the adaptive ability of anti-jamming performance, reduce the computational complexity, and enhance the robustness and reliability of the navigation terminal in a variety of complex interference environments. SUMMARY

[0005] Therefore, the present application provides a space-frequency adaptive anti-jamming improved processing method, device and storage medium to solve the technical problem that the existing space-frequency adaptive anti-jamming processing does not perform targeted processing according to the characteristics of interference signals, and has poor anti-jamming adaptability in different interference scenarios.

[0006] To achieve the above technical purpose, the present application adopts the following technical solutions: In a first aspect, the present application provides a space-frequency adaptive anti-jamming improved processing method, comprising: Obtaining a plurality of analog intermediate frequency signals output by an array antenna after frequency down-conversion by a radio frequency module, and pre-processing the plurality of analog intermediate frequency signals to obtain digital baseband data; Performing fast Fourier transform on the digital baseband data to obtain frequency domain data; intelligently detecting the frequency domain data to determine a type of current interference signal; calculating a covariance matrix according to the frequency domain data, and performing optimization processing on the covariance matrix based on the type of the current interference signal to obtain an optimized covariance matrix; calculating an inverse matrix of the optimized covariance matrix, and calculating a spatial-frequency adaptive weight according to the inverse matrix; performing delay processing on the frequency domain data to obtain delayed frequency domain data; performing spatial-frequency adaptive filtering on the delayed frequency domain data according to the spatial-frequency adaptive weight to obtain interference-suppressed frequency domain data; performing spectrum optimization processing on the interference-suppressed frequency domain data to obtain optimized frequency domain data; performing inverse Fourier transform on the optimized frequency domain data to obtain interference-suppressed time domain data.

[0007] Further, the pre-processing of the multiple analog intermediate frequency input signals to obtain digital baseband data comprises: collecting the multiple analog intermediate frequency signals by using a multi-channel A / D conversion chip to obtain digital intermediate frequency data corresponding to each analog intermediate frequency signal; performing digital down-conversion and FIR low-pass filtering processing on the digital intermediate frequency data to obtain initial digital baseband data; performing down-sampling processing on the initial digital baseband data to obtain down-sampled data; sliding window segmenting the down-sampled data according to a preset frame length, and maintaining a preset overlap ratio between adjacent data segments to form overlap frame data; applying a window function to the overlap frame data to obtain windowed data, wherein the windowed data is the digital baseband data after the pre-processing is completed.

[0008] Further, the intelligently detecting the frequency domain data to determine the type of the current interference signal comprises: performing point-by-point detection on the frequency domain data according to a preset power threshold value; if a power spectral density value of any frequency point exceeds the preset power threshold value, marking the frequency point as an interference frequency point; judging the type of the current interference signal according to the number and position information of the interference frequency points; The type of the interference signal comprises wideband interference, narrowband interference and single-frequency interference.

[0009] Further, the performing optimization processing on the covariance matrix according to the type of the interference signal comprises: The optimization processing formula of the covariance matrix is: In the formula, is the first The statistical covariance matrix of each frequency point To optimize the covariance matrix, It is the identity matrix. The diagonal load factor is used to ensure matrix invertibility under any disturbance scenario. To optimize the factors, These correspond to three different sets of optimization factors: broadband interference, narrowband interference, and single-frequency interference. It is used to perform adaptive operations based on the type of interference, so that the weight calculation approximates the theoretical optimal solution in various scenarios.

[0010] Furthermore, the calculation of the inverse of the optimized covariance matrix and the calculation of the space-frequency adaptive weights based on the inverse matrix include: The space-frequency adaptive weights are calculated using the linearly constrained minimum variance criterion. The calculation formula is as follows: In the formula, For anti-interference weights, It is the inverse matrix. This is the constraint matrix.

[0011] Furthermore, the step of performing space-frequency adaptive filtering on the delayed frequency domain data according to the space-frequency adaptive weights to obtain the interference-suppressed frequency domain data includes: The formula for calculating space-frequency adaptive filtering is: In the formula, For the first The frequency point Data after segment delay For the first The frequency point Frequency domain data after interference suppression of the segment, These are the anti-interference weights.

[0012] Furthermore, the spectral optimization processing of the frequency domain data after interference suppression includes: Frequency-by-frequency power detection is performed on the frequency domain data after interference suppression; If the power value of a certain frequency point is higher than the preset clamping threshold, the complex value of the frequency point is forcibly set to zero, or the power level corresponding to the clamping threshold is used as the upper limit value, and the power of the frequency point is clamped to the upper limit value. The frequency domain data after being zeroed or clamped is output as the spectrum optimization result.

[0013] In a second aspect, the present invention provides a space-frequency adaptive anti-interference improvement processing device, comprising: The data acquisition module is configured to acquire multiple analog intermediate frequency signals output by the array antenna through the radio frequency module, and to obtain digital baseband data by preprocessing the multiple analog intermediate frequency signals. The frequency domain data processing module is configured to perform fast Fourier transform on the digital baseband data to obtain frequency domain data. The adaptive processing module is configured to intelligently detect the frequency domain data to determine a type of a current interference signal, to calculate a covariance matrix based on the frequency domain data, to perform optimization processing on the covariance matrix based on the type of the current interference signal to obtain an optimized covariance matrix, to calculate an inverse matrix of the optimized covariance matrix, to calculate a spatial-frequency adaptive weight based on the inverse matrix, to perform delay processing on the frequency domain data to obtain delayed frequency domain data, to perform spatial-frequency adaptive filtering on the delayed frequency domain data based on the spatial-frequency adaptive weight to obtain interference-suppressed frequency domain data, and to perform spectral optimization processing on the interference-suppressed frequency domain data to obtain optimized frequency domain data. The time domain data processing module is configured to perform inverse Fourier transform on the optimized frequency domain data to obtain interference-suppressed time domain data.

[0014] In a third aspect, the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the spatial-frequency adaptive anti-interference improved processing method.

[0015] Compared with the prior art, the spatial-frequency adaptive anti-interference improved processing method has the following advantages: 1. The covariance matrix optimization processing module is proposed to perform differential processing on the covariance matrix according to the type of the interference signal, to improve the ill-conditioned degree of the covariance matrix in different interference scenarios, to improve the numerical stability of matrix inversion, to reduce the error of matrix inversion and weight calculation, to improve the anti-interference performance of the navigation receiver in different interference scenarios such as wideband, narrowband and single frequency, and to solve the poor anti-interference adaptability of the prior art in different interference scenarios.

[0016] 2. The spectral line optimization processing module is proposed to perform optimization processing on the interference spectral line remaining after spatial-frequency adaptive filtering, to effectively improve the frequency spectrum after interference suppression, and to further improve the anti-interference ability compared with the direct inverse Fourier transform after spatial-frequency adaptive filtering in the prior art.

[0017] 3. The application reduces the implementation complexity by various means, including 1) down-sampling processing of digital baseband data in the pre-processing module, reducing the data amount; 2) only calculating the covariance matrix of the expected signal effective bandwidth frequency point in the covariance matrix optimization processing module, and only calculating the inverse matrix and the space-frequency adaptive weight corresponding to the frequency point in the space-frequency adaptive weight calculation module, which greatly reduces the operation amount compared to calculating all frequency points; 3) Cholesky decomposition is used instead of direct matrix inversion in the space-frequency adaptive weight calculation module, avoiding square root operation and reducing the complexity of matrix inversion.

[0018] In summary, compared with the prior art, the application can obtain excellent anti-interference performance in wideband, narrowband and single-frequency different interference scenarios, while realizing low complexity and high real-time performance, and improving the adaptability of the navigation receiver in a complex electromagnetic environment. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 A flowchart of the space-frequency adaptive anti-interference improved processing method provided by the application is shown in the figure. Figure 2 An execution flowchart of the space-frequency adaptive anti-interference improved processing method provided by the application is shown in the figure. Figure 3 A structure diagram of the space-frequency adaptive anti-interference improved processing device provided by the application is shown in the figure. DETAILED DESCRIPTION

[0020] The preferred embodiments of the application will be described in detail below with reference to the accompanying drawings, wherein the drawings constitute a part of this application and serve to explain the principles of the embodiments of the application, but are not used to limit the scope of the application.

[0021] Embodiment 1 Please see Figure 1 The embodiment provides a space-frequency adaptive anti-interference improved processing method, which comprises the following steps: Step S101: obtaining a plurality of analog intermediate frequency signals output by an array antenna after being down-converted by a radio frequency module, pre-processing the plurality of analog intermediate frequency signals to obtain digital baseband data; Step S102: performing fast Fourier transform on the digital baseband data to obtain frequency domain data; Step S103: intelligently detecting the frequency domain data to determine the type of the current interference signal; Step S104: calculating a covariance matrix based on the frequency domain data, and optimizing the covariance matrix based on the type of the current interference signal to obtain an optimized covariance matrix; Step S105: calculating the inverse matrix of the optimized covariance matrix, and calculating a space-frequency adaptive weight based on the inverse matrix; Step S106: Delay processing is performed on the frequency domain data to obtain delayed frequency domain data; Step S107: Spatial-frequency adaptive filtering is performed on the delayed frequency domain data according to spatial-frequency adaptive weights to obtain interference suppressed frequency domain data; Step S108: Spectrum optimization processing is performed on the interference suppressed frequency domain data to obtain optimized frequency domain data; Step S109: Inverse Fourier transform is performed on the optimized frequency domain data to obtain interference suppressed time domain data.

[0022] The spatial-frequency adaptive anti-interference improvement processing method provided in this embodiment can realize real-time identification of the specific type of the current interference signal by intelligently detecting the frequency domain data corresponding to the multiple analog intermediate frequency signals, and then perform targeted optimization processing on the covariance matrix, dynamically adjust the spatial-frequency adaptive weights, and realize more accurate interference suppression. By optimizing the residual interference spectrum lines of the spatial-frequency adaptive filtering, the interference suppression effect is further improved, and the efficient and stable navigation signal processing performance is ensured in a complex and variable interference environment. At the same time, the processing flow reduces the computational complexity, improves the real-time response ability and anti-interference performance of the system.

[0023] As a specific embodiment, in step S101, the method for obtaining the multiple analog intermediate frequency signals is: obtaining the output of a BeiDou B3 frequency point array antenna after radio frequency down-conversion The multiple analog intermediate frequency signals; wherein, is the number of array elements, and the value range is 4-10, and in this embodiment, is 4, the analog intermediate frequency signal center frequency is 46.52MHz, and the bandwidth is 20.46MHz.

[0024] Further, the multiple analog intermediate frequency input signals are preprocessed, including: Firstly, the multiple analog intermediate frequency signals are collected by using a multi-channel A / D conversion chip to obtain digital intermediate frequency data corresponding to each analog intermediate frequency signal; for the obtained analog intermediate frequency signals, the sampling clock selected in this embodiment is 62MHz, thereby obtaining channel digital intermediate frequency data, the center frequency of the digital intermediate frequency data is 15.48MHz, and the bandwidth is 20.46MHz.

[0025] Secondly, the channel digital intermediate frequency data is subjected to DDC digital down-conversion and FIR low-pass filter processing to obtain channel initial digital baseband data; wherein, the DDC digital down-conversion is used to transform the digital intermediate frequency signal to zero frequency, and the low-pass filter is used to filter out the out-of-band clutter. In this embodiment, the local oscillator signal is generated by a numerically controlled oscillator NCO for digital down-conversion, and the FIR low-pass filter has a 32-order.

[0026] The third step is to downsample the initial digital baseband signal to obtain downsampled data; specifically, for... The digital baseband signal is downsampled by 2 times to obtain In this embodiment, the sampling rate is reduced from 62MHz to 31MHz to reduce the amount of data and save storage and computing resources.

[0027] The fourth step involves dividing the downsampled data into sliding window segments according to a preset frame length, maintaining a preset overlap ratio between adjacent data segments to form overlapping frame data; specifically, each downsampled data segment is divided into segments... Each segment forms a data segment. Adjacent data segments are overlapped to obtain overlapping data. (This is the implementation example.) The value is 128, and the overlap ratio is 50%.

[0028] The fifth step is to apply a window function to the overlapping frame data to obtain windowed data, which is the preprocessed digital baseband data; in this embodiment, the window function type is Hamming window.

[0029] In a preferred embodiment, in step S102, the preprocessed digital baseband data is... A point-wise Fast Fourier Transform (FFT) is used to obtain the frequency domain data. It should be noted that since the FFT implicitly involves matrix windowing and periodic extension of the digital baseband data, a direct FFT can lead to spectral leakage of the interference signal when strong interference is present in the received signal. This is detrimental to the detection and processing of interference lines and affects anti-interference performance. To ensure interference suppression, a Hamming window (non-rectangular window) is applied to the data during the preprocessing stage. This operation smoothly truncates the sequence boundaries and suppresses spectral leakage in the FFT transform. Simultaneously, since direct windowing attenuates the signal and causes a loss in signal-to-noise ratio (SNR), this scheme also employs overlapping processing to further optimize interference suppression and mitigate the SNR loss.

[0030] In a preferred embodiment, in step S103, intelligent detection is performed on the frequency domain data to detect the current type of interference signal, specifically as follows: The frequency domain data is detected point by point according to a preset power threshold. If the power spectral density value of any frequency point exceeds the preset power threshold, the frequency point is marked as an interference frequency point. The type of the current interference signal is determined based on the number and location information of the interference frequency points. In this embodiment, the interference signal is divided into three types according to the bandwidth of the interference signal: broadband, narrowband, and single-frequency. Among them, broadband interference is characterized by dispersed frequency points; narrowband interference is characterized by locally concentrated frequency points; and single-frequency interference is characterized by a prominent single frequency point.

[0031] As a specific implementation, the logic for determining the current interference signal type based on the number and location of detected interference frequency points is as follows: First, all suspicious frequency points are extracted using a power threshold. If only a single frequency point is detected, it is directly judged as single-frequency interference. If more than one frequency point is detected, its maximum continuous bandwidth is calculated. Using a preset bandwidth threshold as the boundary, those with bandwidths not greater than the threshold are judged as narrowband interference, and those with bandwidths greater than the threshold are judged as wideband interference.

[0032] In a preferred embodiment, in step S104, the covariance matrix is ​​calculated based on the frequency domain data, and the covariance matrix is ​​optimized according to the type of interference signal to obtain an optimized covariance matrix, specifically as follows: Assume the first Lu Di Duan Di Individual frequency point data Then the first Duan Di frequency points Road airspace data can be represented as: In the formula, , For the number of array elements, , To count the total number of segments, , For FFT points, yes 3D vector.

[0033] No. Statistical covariance matrix of each frequency point The calculation formula is: In the formula, yes Complex matrix, yes The conjugate transpose of .

[0034] It should be noted that, to reduce the computational load, only the covariance matrix of relevant frequency points within the effective bandwidth of the desired signal can be calculated. In this embodiment, the desired signal bandwidth is 20.46MHz. After a 128-point FFT, only the covariance matrices of frequency points 0-33 and 94-127 need to be calculated, which reduces the computational load by 46.8% compared to calculating 128 frequency points, effectively improving anti-interference real-time performance.

[0035] The covariance matrix is ​​optimized based on the type of the current interference signal to obtain the optimized covariance matrix. The calculation formula is: wherein, is a unit matrix, is a diagonal loading factor, is an optimization factor, respectively correspond to three different optimization factors of wideband interference, narrowband interference and single frequency interference. The diagonal loading factor can ensure the invertibility of the covariance matrix, and the optimization factor can ensure the numerical stability of the covariance matrix of different interference signal types and reduce the error of matrix inversion.

[0036] Specifically, the load to white noise ratio (LNR) is an engineering parameter quantifying the diagonal loading factor relative to the white noise power, and the correlation of the diagonal loading and the system noise is shown in the following formula: wherein, represents the power of the white noise component in the noise, is a diagonal loading factor, and the unit of LNR is dB.

[0037] The diagonal loading factor is generally selected to be less than the eigenvalue of the interference and greater than the eigenvalue of the noise, for solving the ill-conditioned problem of the covariance matrix. According to experience, the load to white noise ratio is more appropriate, and at this time .

[0038] The number of frequency points occupied by different interference signal types is different, and the numerical size of the covariance matrix is different. The numerical size of the covariance matrix is adjusted within a certain range through the optimization factor .

[0039] In actual application, due to the influence of electromagnetic interference, noise, system error, limited sampling number and other factors, the covariance matrix is prone to ill-conditioned problem, resulting in unstable numerical value and large error of matrix inversion, and further resulting in shallow adaptive null and deterioration of anti-interference performance. Through the processing flow of the embodiment, the covariance matrix can be processed differently according to the interference signal type. Compared with the homogeneous processing mode adopted in the prior art, the processing method of the embodiment improves the ill-conditioned degree of the covariance matrix in different interference scenarios, improves the numerical stability of matrix inversion, reduces the error of matrix inversion and weight calculation, improves the anti-interference performance of the navigation receiver in wideband, narrowband, single frequency and other different interference scenarios, and solves the poor anti-interference adaptability problem of the prior art in different interference scenarios.

[0040] As a preferred embodiment, in step S105, the inverse matrix of the optimized covariance matrix is calculated, and the spatial-frequency adaptive weight is calculated according to the inverse matrix, specifically: Since the optimized covariance matrix is a conjugate symmetric positive definite matrix, the embodiment adopts Cholesky decomposition to decompose the optimized covariance matrix, and inverses the decomposed matrix to further obtain the inverse matrix of the optimized covariance matrix, and the steps are as follows: Firstly, the optimized covariance matrix is subjected to Cholesky decomposition to obtain , wherein is a lower triangular matrix, is a diagonal matrix, is the conjugate transpose matrix of , and let , then , the elements of each column of the matrix and the matrix are obtained by cross column; Secondly, the inverse matrix of the matrix is calculated, according to the matrix multiplication formula , the diagonal elements are solved first, and then the elements parallel to the diagonal elements are solved in sequence, wherein is a unit matrix; Thirdly, according to the matrix and the matrix , the inverse matrix of the optimized covariance matrix is calculated, since the optimized covariance matrix is a conjugate symmetric matrix, the inverse matrix thereof is also a conjugate symmetric matrix, so only half of the elements need to be solved.

[0041] Compared with direct matrix inversion, Cholesky decomposition avoids square root operation and reduces computational complexity.

[0042] Further, the embodiment adopts linearly constrained minimum variance (LCMV) criterion, and calculates the spatial-frequency adaptive weight according to the inverse matrix of the optimized covariance matrix, and the calculation formula is as follows: , wherein is an anti-interference weight, is the inverse matrix, is a constraint matrix.

[0043] As a preferred embodiment, in step S106, the frequency domain data is subjected to delay processing to obtain delayed frequency domain data, and the embodiment realizes delay through ram cache, and the delay time depends on the time required for weight calculation, and the purpose is to ensure the matching of the weight and the frequency domain data to realize real-time filtering.

[0044] Specifically, assuming that in actual hardware, the number of clock cycles required from the completion of the FFT output to the complete generation of the space frequency weight W(k) is measured and denoted as L; a dual-port RAM with a depth ≥ L and a width equal to the complex data bit width of the FFT output is built inside the FPGA (or DSP). At the write end, each frequency point X(k) of the FFT output is sequentially written into the RAM; the write address is cyclically incremented; at the read end, "write address − L" is taken as the read address, and after a delay of L clock cycles, it is read out, thus obtaining X_delay(k) synchronized with W(k). Since the read X_delay(k) is time-aligned with the just calculated W(k), the subsequent multiplier can directly perform W(k)·X_delay(k) to achieve zero-error real-time space frequency filtering without the need for additional frame buffering or interrupt waiting.

[0045] In a preferred embodiment, in step S107, the delayed frequency domain data is subjected to space-frequency adaptive filtering to obtain the interference-suppressed frequency domain data, and the calculation formula is as follows: In the formula, For the first The frequency point Data after segment delay For the first The frequency point Frequency domain data after interference suppression of the segment, These are the anti-interference weights.

[0046] In a preferred embodiment, in step S108, the frequency domain data after interference suppression is subjected to spectrum optimization processing to obtain optimized frequency domain data. Since some residual interference spectral lines still exist after space-frequency adaptive filtering, affecting anti-interference performance, spectrum optimization processing is required for the frequency domain data after interference suppression. The residual interference spectral lines can be directly set to zero or placed at a certain clamping threshold. To avoid over-processing leading to signal-to-noise ratio degradation, the frequency domain data can be sorted based on amplitude, and the number of spectral lines to be processed can be adaptively selected according to the type of interference signal. The specific steps are as follows: Frequency-by-frequency power detection is performed on the frequency domain data after interference suppression; If the power value of a certain frequency point is higher than the preset clamping threshold, the complex value of the frequency point is forcibly set to zero, or the power level corresponding to the clamping threshold is used as the upper limit value, and the power of the frequency point is clamped to the upper limit value. The frequency domain data after being zeroed or clamped is output as the spectrum optimization result.

[0047] like Figure 2 As shown, Figure 2 A schematic diagram illustrating the execution flow of the method in this embodiment is shown.

[0048] Embodiment 2 As Figure 3 shown, the embodiment of the present application further provides a space-frequency adaptive anti-interference improved processing device, comprising: a data acquisition module 301, configured to acquire a plurality of analog intermediate frequency signals output by an array antenna after frequency down-conversion by a radio frequency module, and to obtain digital baseband data by preprocessing the plurality of analog intermediate frequency signals; a frequency domain data processing module 302, configured to perform fast Fourier transform on the digital baseband data to obtain frequency domain data; an adaptive processing module 303, configured to intelligently detect the frequency domain data to determine a type of a current interference signal, to calculate a covariance matrix based on the frequency domain data, to perform optimization processing on the covariance matrix based on the type of the current interference signal to obtain an optimized covariance matrix, to calculate an inverse matrix of the optimized covariance matrix, to calculate space-frequency adaptive weights based on the inverse matrix, to perform delay processing on the frequency domain data to obtain delayed frequency domain data, to perform space-frequency adaptive filtering on the delayed frequency domain data based on the space-frequency adaptive weights to obtain interference-suppressed frequency domain data, and to perform spectrum optimization processing on the interference-suppressed frequency domain data to obtain optimized frequency domain data; a time domain data processing module 304, configured to perform inverse Fourier transform on the optimized frequency domain data to obtain interference-suppressed time domain data.

[0049] Embodiment 3 The embodiment provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the space-frequency adaptive anti-interference improved processing method according to any one of the technical solutions.

[0050] The computer readable storage medium and the computing device provided by the above embodiments of the present application can be implemented according to the content described in the description of the space-frequency adaptive anti-interference improved processing method, and have similar beneficial effects to the space-frequency adaptive anti-interference improved processing method, and thus the description is omitted here.

[0051] The above description is only the preferred embodiment of the present application, and the protection scope of the present application is not limited to this. Any changes or replacements within the technical scope disclosed by the present application can be easily thought by those skilled in the art, and should be covered within the protection scope of the present application.

Claims

1. A space-frequency adaptive interference rejection processing method, characterized in that, The method comprises the following steps: Obtaining multiple analog intermediate frequency signals output by a radio frequency module of an array antenna, and pre-processing the multiple analog intermediate frequency signals to obtain digital baseband data; Performing fast Fourier transform on the digital baseband data to obtain frequency domain data; Intelligently detecting the frequency domain data to determine a type of a current interference signal; Calculating a covariance matrix based on the frequency domain data, and performing optimization processing on the covariance matrix based on the type of the current interference signal to obtain an optimized covariance matrix; Calculating an inverse matrix of the optimized covariance matrix, and calculating a spatial-frequency adaptive weight based on the inverse matrix; Performing delay processing on the frequency domain data to obtain delayed frequency domain data; Performing spatial-frequency adaptive filtering on the delayed frequency domain data based on the spatial-frequency adaptive weight to obtain interference-suppressed frequency domain data; Performing spectrum optimization processing on the interference-suppressed frequency domain data to obtain optimized frequency domain data; Performing inverse Fourier transform on the optimized frequency domain data to obtain interference-suppressed time domain data.

2. The space-frequency adaptive interference rejection processing method of claim 1, wherein The pre-processing of the multiple analog intermediate frequency input signals to obtain the digital baseband data comprises the following steps: Collecting the multiple analog intermediate frequency signals by using a multi-channel A / D conversion chip to obtain digital intermediate frequency data corresponding to each analog intermediate frequency signal; Performing digital down-conversion and FIR low-pass filtering processing on the digital intermediate frequency data to obtain initial digital baseband data; Performing down-sampling processing on the initial digital baseband data to obtain down-sampled data; Sliding window segmenting the down-sampled data according to a preset frame length, and maintaining a preset overlap ratio between adjacent data segments to form overlap frame data; Applying a window function to the overlap frame data to obtain windowed data, which is the digital baseband data after the pre-processing.

3. The space-frequency adaptive interference rejection processing method of claim 1, wherein, The intelligent detection of the frequency domain data to determine the type of the current interference signal comprises the following steps: Performing point-by-point detection on the frequency domain data according to a preset power threshold; if a power spectral density value of any frequency point exceeds the preset power threshold, the frequency point is marked as an interference frequency point; Judging the type of the current interference signal according to the number and position information of the interference frequency points; The type of the interference signal includes wideband interference, narrowband interference and single-frequency interference.

4. The space-frequency adaptive interference rejection processing method of claim 3 wherein, The optimization processing of the covariance matrix according to the type of the interference signal comprises the following steps: The optimization processing formula of the covariance matrix is: In the formula, is the statistical covariance matrix of the first frequency point, is the optimized covariance matrix, is the unit matrix, is the diagonal loading factor, used to ensure that the matrix is reversible under any interference scenario, is the optimization factor, corresponding to three different optimization factors of wideband interference, narrowband interference and single-frequency interference respectively, for adaptive operation according to the type of interference, so that the weight calculation approximates the theoretical optimal solution under various scenarios.

5. The space-frequency adaptive interference rejection processing method of claim 1, wherein, The calculation of the inverse matrix of the optimized covariance matrix and the calculation of the spatial-frequency adaptive weight based on the inverse matrix comprise the following steps: The spatial-frequency adaptive weight is calculated by using a linear constraint minimum variance criterion, and the calculation formula is: wherein is an anti-interference weight, is an inverse matrix, is a constraint matrix.

6. The space-frequency adaptive interference rejection processing method of claim 5 wherein, The spatial-frequency adaptive filtering of the delayed frequency domain data based on the spatial-frequency adaptive weight to obtain the interference-suppressed frequency domain data comprises the following steps: The calculation formula of the spatial-frequency adaptive filtering is: In the formula, is the first frequency point segment delay data, is the first frequency point segment interference suppression frequency domain data, is the anti-interference weight.

7. The space-frequency adaptive interference rejection processing method of claim 1 wherein, The spectrum optimization processing of the interference-suppressed frequency domain data comprises the following steps: Performing frequency-by-frequency power detection on the interference-suppressed frequency domain data; If a power value of a certain frequency point is higher than a preset clamping threshold, the complex value of the frequency point is forced to be zero, or the power of the frequency point is clamped to an upper limit value corresponding to the clamping threshold; The frequency domain data after the zeroing or clamping processing is output as a spectrum optimization result.

8. A space-frequency adaptive anti-interference improvement processing device, characterized in that, The method comprises the following steps: The data acquisition module is configured to acquire multiple analog intermediate frequency signals output by the array antenna through the radio frequency module, and to obtain digital baseband data by preprocessing the multiple analog intermediate frequency signals. The frequency domain data processing module is configured to perform fast Fourier transform on the digital baseband data to obtain frequency domain data. The adaptive processing module is configured to intelligently detect the frequency domain data to determine a type of a current interference signal. A covariance matrix is calculated based on the frequency domain data, and the covariance matrix is optimized based on the type of the current interference signal to obtain an optimized covariance matrix. An inverse matrix of the optimized covariance matrix is calculated, and a space-frequency adaptive weight is calculated based on the inverse matrix. The frequency domain data is delayed to obtain delayed frequency domain data. The delayed frequency domain data is subjected to space-frequency adaptive filtering based on the space-frequency adaptive weight to obtain interference-suppressed frequency domain data. The interference-suppressed frequency domain data is subjected to spectrum optimization to obtain optimized frequency domain data.

9. A computer-readable storage medium, characterized in that, The time domain data processing module is configured to perform inverse Fourier transform on the optimized frequency domain data to obtain interference-suppressed time domain data. The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the space-frequency adaptive anti-interference improvement processing method of any one of claims 1-7.

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