Feature-Compression-Based Detection of Sea-Surface Small Targets

Abstract

This paper aims to develop a feature-based detector using seven existing salient features of radar returns to improve the detection ability of high-resolution maritime ubiquitous radars to sea-surface small targets. Maritime ubiquitous radars form simultaneously dwelling beams at multiple azimuths by digital array receiver and allow long observation time for detection. Due to absence or incompletion of training samples of radar returns with various types of sea-surface small targets, the detection boils down to designing a one-class classifier in the seven-dimensional (7D) feature space mainly by using training samples of sea clutter. A feature compression method, though maximizing interclass Bhattacharyya distance, is proposed to compress the 7D feature vector into one 3D feature vector with the help of simulated radar returns of typical targets. In the compressed 3D feature space, a modified convexhull learning algorithm is given to determine one convex polyhedron decision region of sea clutter at a given false alarm rate. In this way, a feature-compression-based detector is constructed, which can exploit more features of radar returns to improve detection performance. It is verified by the recognized and open IPIX and CSIR radar databases for sea-surface small target detection. The results show that it attains obvious performance improvement.

Keywords

• High-resolution maritime ubiquitous radars
• sea-surface small targets
• feature compression
• convexhull learning
• one-class classifier
• feature-compression-based detector