A Synthetic Aperture Radar Anti-Spoofing Jamming Method Based on Shadow Characteristics

Abstract

The present invention proposes a synthetic aperture radar anti-spoofing jamming method based on shadow characteristics. It first uses the synthetic aperture radar imaging method and the electromagnetic scattering simulation method to obtain several types of SAR images with shadows and no shadows in different attitudes. , and the SAR images obtained under different radar incident angles are used as training samples and test samples of the convolutional neural network respectively; Targets and backgrounds are classified to obtain different types of targets and backgrounds. Standard threshold segmentation methods and multi-value processing are used for key target images to obtain multi-value images after target area segmentation. Convolutional neural network classification methods are used to distinguish real and deceptive targets. The invention simultaneously realizes the functions of SAR automatic target recognition and interference target recognition, and realizes high-performance SAR anti-deception interference in the image domain.

Description

technical field [0001] The invention belongs to the field of radar technology, in particular to the technical field of synthetic aperture radar (SAR) anti-jamming and synthetic aperture radar (SAR) automatic target recognition (Automatic Target Recognition, ATR) technical field. Background technique [0002] Deceptive jamming achieves the purpose of disrupting the opponent's radar reconnaissance system by simulating the echo signals of false targets or false scenes. As the jammer simulates the SAR echo signal more precisely, the fineness of jamming modulation has been significantly improved, so that the Doppler coherence of the real echo can be simulated more accurately, and the power requirement for the jammer is greatly reduced , and can form more refined deception interference results. International scholars have conducted research on the basic principles of spoofing jamming. Deception jamming has coherence in both the range and azimuth directions. In imaging processing,...

AI Technical Summary

Problems solved by technology

Then, aiming at the shortcomings of convolutional networks that are not effective in recognizing shadow features, the present invention proposes a two-level classification strategy, that is, using the first-level convolutional neural network to classify objects and backgrounds to obtain different types of objects and backgrounds, and then For key targets (such as ground targets, air targets, etc.) images, the standard threshold segmentation method and multi-valued processing are used to obtain the multi-valued image after the target area is segmented. Finally, the convolutional neural network classification method is used for the multi-valued processed samples. , to distinguish the real target from the spoofed target

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