Sea Clutter and Target Detection with Deep Neural Networks

Abstract

In this paper, we investigate the feasibility of applying a deep learning approach to sea clutter suppression and target detection in an inhomogeneous oceanic environment. The employed method consists of deep convolutional auto-encoders (DCAEs) to filter sea clutter and a logistic regression classifier to achieve the detection of target. The sea clutter suppression processing using DCAEs automatically removes complex patterns like superimposed clutter from a target, rather than simple patterns like echoes missing at random. Compared with conventional methods for sea clutter suppression, the algorithm does not need to estimate the covariance matrix of clutter so as to have better flexibility. The results show that reliable suppression performance and higher detection accuracy can be achieved from our experiments, whose data include the measured data and simulation data.