Interrupted sampling repeater jamming suppression based on multiple extended complex-valued convolutional auto-encoders

Abstract

Interrupted sampling repeater jamming (ISRJ) with flexible modulation parameters and coherent processing gain seriously threatens the radar detection system. The jamming suppression and target detection performance of existing anti-jamming methods are limited by strong noise and jamming signals. An ISRJ suppression method combining multiple extended complex-valued convolutional auto-encoders (CVCAEs) and compressed sensing (CS) reconstruction is proposed. For the different tasks of parameter estimation and signal denoising, the extended CVCAEs including a complex-valued convolutional shrinkage network (CVCSNet) and a complex-valued UNet (CVUNet) are developed. Based on the time-domain discontinuity of ISRJ signals, the CVCSNet is first used to directly estimate the parameters representing signal components and extract jamming-free signals from received signals. Then, the extracted signals are denoised using the CVUNet. After that, relying on the denoised signals and the frequency sparsity of de-chirped target signals, a CS model is established and solved to recover complete target signals for jamming suppression. Utilising the advantages of deep neural networks in weak feature extraction and signal representation, the CVCSNet and CVUNet can effectively improve the signal extraction accuracy and alleviate the limitation of noise on target signal reconstruction. Experimental results verify that the proposed method has superior ISRJ suppression performance and is robust to varying signal-to-noise ratios, jamming-to-signal ratios and jamming parameters.