A method for detecting clutter from a SAR single look complex image

Abstract

The application provides a method for detecting interference from a SAR single-view complex image, comprising the following steps: performing distance Fourier transform on a SAR image to be processed; performing equalization processing on a distance spectrum of the SAR image; equally dividing the distance spectrum into a plurality of subbands; performing inverse distance Fourier transform on each subband spectrum to obtain a plurality of subband images; calculating the contrast of each pixel in the subband domain; and comparing the calculated contrast with a threshold value, and detecting a pixel as interference if the contrast exceeds the threshold value. The application can effectively detect intermittent sampling repeater interference and common unintentional interference from a SAR image.

Description

Technical Field

[0001] This invention belongs to the field of radar signal processing technology, specifically a method for detecting interference from SAR single-look complex images. Background Technology

[0002] With its all-weather, all-day, high-resolution imaging capabilities, Synthetic Aperture Radar (SAR) has become an indispensable observation payload for combat units such as satellites, missiles, and various aircraft. However, SAR also faces serious threats from electronic warfare: the enemy can interfere with SAR in various ways, blinding it or producing false images, thereby weakening its effectiveness. SAR jamming techniques mainly fall into two categories: passive and active. Among them, active jamming poses a greater threat to SAR. Typical active jamming includes: noise jamming, frequency shift jamming, frequency sweeping jamming, convolutional deception jamming, intermittent sampling and forwarding jamming, scattered wave jamming, and motion modulation jamming. Intermittent sampling and forwarding jamming requires fewer parameters to detect and is easier for jammers to implement. All of the above jamming falls under the category of intentional jamming. Besides these intentional jammings, SAR also faces numerous unintentional jammings caused by spectrum sharing. Suppressing these jammings requires that the jamming be detected in the SAR data. In recent years, scholars have conducted some research on SAR jamming detection, but this research mainly focuses on unintentional jamming, with relatively little research on the detection of intentional jamming. Summary of the Invention

[0003] This invention proposes a method for detecting interference from SAR single-look complex images, which can effectively detect intermittent sampling forwarding interference and common unintentional interference. The detection results can be used for adaptive interference suppression.

[0004] The technical solution for achieving the objective of this invention is: a method for detecting interference from SAR single-look complex images, comprising:

[0005] Step 1: Perform a range-to-Fourier transform on the SAR image to be processed to obtain the range spectrum matrix;

[0006] Step 2: Perform range spectrum equalization processing on the SAR image;

[0007] Step 3: Divide the equalized range spectrum into multiple sub-bands at equal intervals;

[0008] Step 4: Perform a distance-to-Fourier inverse transform on the spectrum of each sub-band to obtain multiple sub-band images.

[0009] Step 5: Calculate the contrast of each pixel in the sub-band domain.

[0010] Step 6: Compare the calculated sub-band contrast of each pixel with the threshold. If the contrast is greater than the threshold, it is determined to be an interference pixel.

[0011] Preferably, step 1 is performed as follows:

[0012] The SAR single-look complex image is represented by a matrix I, and the image size is N. a ×N r Perform distance-to-N direction mapping on this matrix. r Point Fourier transform yields the distance spectrum matrix S.

[0013] Preferably, the specific method for equalizing the range spectrum of the SAR image in step 2 is as follows:

[0014] Multiply the second dimension of the range spectrum matrix S by the reciprocal of the range window function to make the range spectrum a rectangular window. If oversampling exists in the range direction, invalid data outside the data bandwidth is set to 0. The equalized data is denoted as S. eq .

[0015] Preferably, in step 3, the equalized range spectrum is divided into K sub-matrices at equal intervals within the range bandwidth, and the k-th sub-matrix is ​​denoted as S. eq,k .

[0016] Preferably, the sub-band contrast C[n,m] of pixel [n,m] is calculated using the following formula:

[0017]

[0018] In the formula, K is the total number of child images, and I k [n,m] represents the k-th sub-band image.

[0019] Compared with the prior art, the significant advantages of this invention are: it can effectively distinguish between interfering and non-interfering regions, and the calculation is simple and easy to implement.

[0020] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0021] Figure 1 To equalize the range-direction spectral amplitude before the event.

[0022] Figure 2 This refers to the range-direction spectral amplitude after equalization.

[0023] Figure 3 It consists of 10 sub-band images.

[0024] Figure 4 The result is the sub-band contrast calculation.

[0025] Figure 5 This is the result of intermittent sampling interference detection. Detailed Implementation

[0026] The present invention, namely a method for detecting interference from SAR single-view complex images, is further described below with reference to the accompanying drawings and examples.

[0027] A method for detecting interference in SAR single-look complex images involves: performing a range-to-Fourier transform on the SAR image to be processed; equalizing the range spectrum of the SAR image; dividing the range spectrum into multiple sub-bands at equal intervals; performing an inverse range-to-Fourier transform on the spectrum of each sub-band to obtain multiple sub-band images; calculating the contrast of each pixel in the sub-band domain; comparing the calculated contrast with a threshold, and detecting pixels as interference if the contrast exceeds the threshold. The specific steps are as follows:

[0028] Step 1: Perform a range-to-Fourier transform on the SAR image to be processed. Represent the SAR single-look complex image with a matrix I, and the image size is N. a ×N r (Azimuth X and Range). Perform range N on this matrix. r Point Fourier transform yields the distance spectrum matrix S.

[0029] Step 2: Equalize the range spectrum of the SAR image. Multiply the second dimension of matrix S by the reciprocal of the range window function to make the range spectrum a rectangular window. If oversampling exists in the range direction, invalid data outside the data bandwidth should be set to 0. The equalized data is denoted as S. eq . Figure 1 The range-direction spectral amplitude before equalization is given. Figure 2 The range-direction spectral amplitude after equalization is given.

[0030] Step 3: Divide the range spectrum into multiple sub-bands at equal intervals. (The sentence fragment "S" appears to be incomplete and lacks context. It's unclear what "S" refers to.) eq The distance bandwidth is divided into K sub-matrices at equal intervals, and the k-th sub-matrix is ​​denoted as S. eq,k These matrices are the subband spectrum matrices.

[0031] Step 4: Convert the spectrum matrix S of each sub-band eq,k Perform distance to N r The point-wise inverse Fourier transform yields K sub-band images, with the k-th sub-band image denoted as I. k . Figure 3 Ten examples of generated subband images are given, which contain false targets caused by a series of intermittent sampling and forwarding interference.

[0032] Step 5: Calculate the contrast of each pixel in the sub-band domain. Let C[n,m] be the sub-band contrast of the [n,m]th pixel, and its calculation expression is:

[0033]

[0034] Figure 4 This is an example of subband contrast calculation results. High-contrast regions correspond to intermittent sampling and forwarding interference.

[0035] Step 6: Compare the calculated sub-band contrast of each pixel with the threshold. If the contrast is greater than the threshold, it is determined to be an interfering pixel. Let the threshold be ξ. If C[n,m]>ξ, then the [n,m]th pixel has interference; otherwise, there is no interference. Figure 5 An example of intermittent sampling interference detection results is given.

Claims

1. A method for detecting interference from SAR single-look complex images, characterized in that, include: Step 1: Perform a range-to-Fourier transform on the SAR image to be processed to obtain the range spectrum matrix; Step 2: Perform range spectrum equalization processing on the SAR image; Step 3: Divide the equalized range spectrum into multiple sub-bands at equal intervals; Step 4: Perform an inverse distance-to-Fourier transform on the spectrum of each sub-band to obtain multiple sub-band images; Step 5: Calculate the contrast of each pixel in the sub-band domain; Step 6: Compare the calculated sub-band contrast of each pixel with the threshold. If the contrast is greater than the threshold, it is determined to be an interference pixel.

2. The method for detecting interference from SAR single-look complex images according to claim 1, characterized in that, The specific steps for step 1 are as follows: The SAR single-look complex image is represented by a matrix I, and the image size is N. a ×N r Perform distance-to-N direction mapping on this matrix. r Point Fourier transform yields the distance spectrum matrix S.

3. The method for detecting interference from SAR single-look complex images according to claim 1, characterized in that, The specific method for equalizing the range spectrum of the SAR image in step 2 is as follows: Multiply the second dimension of the range spectrum matrix S by the reciprocal of the range window function to make the range spectrum a rectangular window. If oversampling exists in the range direction, invalid data outside the data bandwidth is set to 0. The equalized data is denoted as S. eq .

4. The method for detecting interference from SAR single-look complex images according to claim 1, characterized in that, Step 3 divides the equalized range spectrum into K sub-matrices at equal intervals within the range bandwidth, and the k-th sub-matrix is ​​denoted as S. eq,k .

5. The method for detecting interference from SAR single-look complex images according to claim 1, characterized in that, The sub-band contrast C[n,m] of pixel [n,m] is calculated using the following formula: In the formula, K is the total number of sub-band images, and I k [n,m] represents the k-th sub-band image.

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