Robust Adaptive Pulse Compression for Synthetic Aperture Radar

Abstract

Traditional matched filter is created based on the maximum output signal-noise-ratio (SNR) via the temporal conjugate reverse of the transmitted signal. However, matched filter treats every sampling point in the same way and results in strong targets covering the neighboring weak targets for their high-level side lobes. To mitigate the masking influence, an adaptive pulse compression (APC) was developed via updating the filter’s tap coefficients for each output point. Unfortunately, intolerable processing time blocks the wide use of APC. In this paper, a fast implementation named iterative adaptive pulse compression (IAPC), is proposed whereby the respective tap coefficients of all filters are jointly updated with a recursive way and subsequently we incorporate it into the classic range Doppler algorithm via changing the processing sequence. Moreover, an encoder-decoder convolutional neural network (CNN) is developed for boosting the targets’ levels with taking into account IAPC severely being influenced by SNR. As a result, through a variety of stressing experiments, the proposed method is shown to be superior to the original APC and matched filter in the views of time efficiency and peak sidelobe ratio respectively.