Sea Clutter Suppression Based on Selective Reconstruction of Features

Abstract

This paper presents a sea clutter suppression (SCS) method for single-channel maritime radar based on a deep Convolutional Neural Network (SCS-CNN), which consists of an encoder and a decoder. First, the encoder is used to extract depth features of the original Range-Doppler spectrum obtained from sub-aperture echoes. Second, the decoder is used to selectively reconstruct the desired Range-Doppler spectrum which only contains the moving targets. Finally, the results of moving target detection are obtained by using the cell average constant false alarm rate detector. This method effectively suppresses the sea clutter and correctly indicates the moving targets under different Signal-to-Clutter-plus-Noise Ratios with low false alarm rate. Particularly, it has good feature extraction and reconstruction abilities for the moving targets whose Doppler is inside the mainlobe clutter.