An Effective Method for Weak Multi-target Detection and Tracking in Clutter Environment

Abstract

Weak target detection and tracking is a difficult problem, especially in the case of multi-target and strong clutters. Track-before-detect (TBD) is the common method to deal with this problem, and this paper proposes a new effective method based on TBD. Firstly, keystone transform (KT) and phase gradient autofocus (PGA) are used for migration compensation to improve the signal-to-noise ratio (SNR) of moving targets. Then dynamic programming based TBD (DP-TBD) with joint intensity-spatial CFAR (J-CA-CFAR) is presented for noncoherent integration, where J-CA-CFAR uses both intensity and spatial information to achieve automatic target detection. Finally, the effectiveness of the proposed method was demonstrated by experimental results on real data.