A method for suppressing communication interference in sea clutter based on linear prediction

Abstract

The present invention discloses a method for suppressing communication interference in sea clutter based on linear prediction, comprising the following steps: Step 1, radar receiving echo data is recorded as X, where X is an M*K dimensional complex matrix; Step 2, marking whether each pulse has an interference signal to form a K-dimensional label vector; Step 3, performing discrete Fourier transform on the complex matrix; Step 4, calculating the interference detection threshold; Step 5, performing interference filtering on the range frequency domain data of the pulse; Step 6, performing inverse discrete Fourier transform on the matrix to obtain a time domain data matrix after interference suppression. The method disclosed in the present invention is based on the time correlation between sea clutter and target data, and linearly predicts and interpolates the interference values ​​of different frequency points using adjacent non-interference data, thereby better maintaining the target signal while achieving interference suppression, so that the radar target detection probability after interference suppression is improved.

Description

Technical Field

[0001] The present invention belongs to the field of interference suppression research, and particularly relates to a method for suppressing communication interference in sea clutter based on linear prediction in this field, which can effectively solve the problem of performance degradation of radar sea detection caused by radio communication interference. Background Art

[0002] P-band radars offer long-range early warning and counter-stealth capabilities. However, with the continuous advancement of radio technology, the electromagnetic environment in which radars operate is becoming increasingly complex. The approved frequency range for FM transceivers is 31–470 MHz, while the UHF band for television signals ranges from 300 MHz to 3000 MHz. This makes P-band radars highly susceptible to wireless communication interference. For radar target detection, interference can lead to false or missed detections. Therefore, interference detection and suppression in sea clutter for P-band radars is a worthy research and solution.

[0003] There has been extensive research on interference suppression both domestically and internationally, which can be broadly categorized into two main approaches: non-parametric and parametric. Parametric methods are based on the model assumption that RF interference consists of a series of sinusoidal signals, and the interference signal is obtained by estimating the model parameters. However, the performance of this method is affected by the accuracy of the model and the computational complexity is high. Classic non-parametric methods include the frequency domain notch method and the subspace projection method. The subspace projection method has a high computational complexity, while the frequency domain notch method is computationally simple and is the most common method. The traditional frequency domain notch method directly sets the interference frequency point to zero, resulting in loss of the spectral components of the target echo. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method for suppressing communication interference in sea clutter based on linear prediction, which can effectively suppress communication interference in sea clutter while better maintaining the target signal, thereby improving the radar's detection capability of the target.

[0005] The present invention adopts the following technical solutions:

[0006] A method for suppressing communication interference in sea clutter based on linear prediction is improved in that it includes the following steps:

[0007] Step 1: The radar received echo data is recorded as X, where X is an M×K dimensional complex matrix, and M and K represent the number of range units and pulses of the radar echo data, respectively.

[0008] Step 2: Mark each pulse for the presence or absence of an interference signal to form a K-dimensional label vector l(k):

[0009]

[0010] Step 3: Perform discrete Fourier transform on the complex matrix X to form a range-frequency domain data matrix Y of radar echo data, and take L consecutive columns of interference-free signal data from the matrix Y to form a range-frequency domain data matrix Z of interference-free data;

[0011] Step 4: Calculate the interference detection threshold T based on the distance frequency domain data matrix Z of the interference-free data m :

[0012]

[0013] Where z(m,l) is the element in the mth row and lth column of the matrix Z;

[0014] Step 5: The first interference pulse adjacent to the non-interference data is numbered as k0, the range frequency domain data of the k0th pulse is subjected to interference filtering, and then the k0th pulse is marked as non-interference data, i.e., l(k0)=0. Specifically:

[0015] Step 51, take the k0th column element in the matrix Y, and determine the position where the interference signal appears in the range frequency domain as {m j ,j=1,2,...,J}, satisfying y(m j ,k0) is the mth j row, k0th column element, J represents the number of frequency points with interference;

[0016] Step 52: Set the AR model order P=10. When the non-interference pulse appears before k0, use the mth order in the range-frequency domain data matrix Z of the non-interference data. j The Burg algorithm is used to calculate the forward prediction coefficients a1, a2, ..., a of the sea clutter data. P Otherwise, calculate the backward prediction coefficients b1, b2, ..., b P ;

[0017] Step 53: Replace the mth matrix in the matrix Y with the linear prediction result. j The element value of row, column k0:

[0018]

[0019] Step 6: Repeat step 5 until all pulses are marked as interference-free data, perform inverse discrete Fourier transform on the matrix Y, and obtain the interference-suppressed time domain data matrix X′.

[0020] The beneficial effects of the present invention are:

[0021] The method disclosed in the present invention is based on the time correlation between sea clutter and target data. It linearly predicts and interpolates the interference values ​​of different frequency points using adjacent interference-free data, thereby better maintaining the target signal while achieving interference suppression, thereby improving the probability of radar target detection after interference suppression. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flowchart of the method of the present invention;

[0023] Figure 2 This is a distance-pulse amplitude diagram of radar echo data measured by a shore-based P-band radar used in the experiment;

[0024] Figure 3 The method of the present invention is used to Figure 2 The radar echo data shown is a distance-pulse amplitude diagram after interference suppression processing;

[0025] Figure 4 The method of the present invention is used to Figure 2 The figure shows the MTD target detection results after the radar echo data is processed for interference suppression. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0027] Example 1: This embodiment discloses a method for suppressing communication interference in sea clutter (frequency domain filtering) based on linear prediction, such as Figure 1 As shown, the following steps are included:

[0028] Step 1: The radar received echo data is recorded as X, where X is an M×K dimensional complex matrix, and M and K represent the number of range units and pulses of the radar echo data, respectively.

[0029] Step 2: Mark each pulse for the presence or absence of an interference signal to form a K-dimensional label vector l(k):

[0030]

[0031] Step 3: Perform discrete Fourier transform on the complex matrix X to form a range-frequency domain data matrix Y of radar echo data, and extract L consecutive columns of interference-free signal data from the complex matrix Y to form a range-frequency domain data matrix Z of interference-free data;

[0032] That is, Y = fft(X), where fft(·) is the fast Fourier transform function in MATLAB;

[0033] Step 4: Calculate the interference detection threshold T based on the distance frequency domain data matrix Z of the interference-free data m :

[0034]

[0035] Where z(m,l) is the element in the mth row and lth column of the matrix Z;

[0036] Step 5: The first interference pulse adjacent to the non-interference data is numbered as k0, the range frequency domain data of the k0th pulse is subjected to interference filtering, and then the k0th pulse is marked as non-interference data, i.e., l(k0)=0. Specifically:

[0037] Step 51, take the k0th column element in the matrix Y, and determine the position where the interference signal appears in the range frequency domain as {m j ,j=1,2,...,J}, satisfying y(m j ,k0) is the mth j row, k0th column element, J represents the number of frequency points with interference;

[0038] Step 52: Set the AR model order P=10. When the non-interference pulse appears before k0, use the mth order in the range-frequency domain data matrix Z of the non-interference data. j The Burg algorithm is used to calculate the forward prediction coefficients a1, a2, ..., a of the sea clutter data. P Otherwise, calculate the backward prediction coefficients b1, b2, ..., b P ;

[0039] Step 53: Replace the mth matrix in the matrix Y with the linear prediction result. j The element value of row, column k0:

[0040]

[0041] Step 6: Repeat step 5 until all pulses are marked as interference-free data. Perform an inverse discrete Fourier transform on matrix Y to obtain the interference-suppressed time-domain data matrix X′. That is, X′=ifft(Y), where ifft(·) is the inverse fast Fourier transform function in MATLAB.

[0042] The effect of the method of the present invention can be further illustrated by the following experiments:

[0043] The data used in the experiment are radar echo data of a shore-based P-band radar measuring the sea. The radar pulse repetition frequency is 1000Hz and the measurement time is about 1 minute. The radar echo data distance-pulse amplitude diagram is shown in the figure below. Figure 2As shown in the figure, the lighter the color, the larger the amplitude. The bright stripes along the distance dimension are interference signals, and the bright stripes along the pulse dimension are ship-like target signals. Figure 2 The radar echo data shown in the figure is the distance-pulse amplitude diagram after interference suppression processing. Figure 3 As shown, compared Figure 2 It can be seen that the method of the present invention effectively suppresses interference signals.

[0044] The MTD target detection algorithm is used to perform target detection processing to illustrate the benefits of the method of the present invention compared with the existing methods. Set the false alarm probability to 10 -3 , the number of reference units is 30, the number of protection units is 2, and the target detection results after interference suppression using the method of the present invention are as follows Figure 4 As shown in the figure, the white point is the point detected as the target. The detection probability of the moving target pointed by the white arrow in the figure is 0.51 when no interference suppression is performed. The detection probability of the traditional frequency domain notch method after interference suppression is 0.53, while the detection probability of the method of the present invention after interference suppression is 0.66, indicating that the method of the present invention maintains the target signal well while performing interference suppression, thereby improving the radar target detection probability, which illustrates the beneficialness of the method of the present invention.

Claims

1. A method for suppressing communication interference in sea clutter based on linear prediction, characterized in that: The steps include: Step 1: The radar received echo data is recorded as X, where X is an M×K dimensional complex matrix, and M and K represent the number of range units and pulses of the radar echo data, respectively. Step 2: Mark each pulse for the presence or absence of an interference signal to form a K-dimensional label vector l(k): Step 3: Perform discrete Fourier transform on the complex matrix X to form a range-frequency domain data matrix Y of radar echo data, and take L consecutive columns of interference-free signal data from the matrix Y to form a range-frequency domain data matrix Z of interference-free data; Step 4: Calculate the interference detection threshold T based on the distance frequency domain data matrix Z of the interference-free data m : Where z(m,l) is the element in the mth row and lth column of the matrix Z; Step 5: The first interference pulse adjacent to the non-interference data is numbered as k0, the range frequency domain data of the k0th pulse is subjected to interference filtering, and then the k0th pulse is marked as non-interference data, i.e., l(k0)=0. Specifically: Step 51, take the k0th column element in the matrix Y, and determine the position where the interference signal appears in the range frequency domain as {mj, j = 1, 2, ..., J}, satisfying y(m j ,k0) is the mth j row, k0th column element, J represents the number of frequency points with interference; Step 52: Set the AR model order P=10. When the non-interference pulse appears before k0, use the mth order in the range-frequency domain data matrix Z of the non-interference data. j The Burg algorithm is used to calculate the forward prediction coefficients a1, a2, ..., a of the sea clutter data. P Otherwise, calculate the backward prediction coefficients b1, b2, ..., b P ; Step 53: Replace the mth matrix in the matrix Y with the linear prediction result. j The element value of row, column k0: Step 6: Repeat step 5 until all pulses are marked as interference-free data, perform inverse discrete Fourier transform on the matrix Y, and obtain the interference-suppressed time domain data matrix X′.

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