Suppressing Ghost Targets via Gating and Track History in Y-Shaped Passive Linear Array Sonars

Abstract

In Y-shaped passive linear array sonar (PLAS) systems composed of 3 sensor legs with consecutively missed detections in a full scan, the complex ghosts that occur due to bearing ambiguity seriously deteriorate the target tracking performance. A solution to the ghost problem is proposed which exploits the track information and geometrical relationship between each PLAS leg and the target. The region of possible ghost targets is first established by making use of the geometrical relationship of the PLAS with respect to the target. The ghost targets are then eliminated by means of the updated track information refined via a combination of 3-D assignment with gating and judicial track management. In the track management and gating procedure, we propose an optimal measurement selection criterion based on maximum joint probability data association (MJPDA), which combines both the soft and hard association techniques. The established tracks are updated by the Kalman filter with MJPDA and are terminated with a logic-based procedure. Representative simulations demonstrate the effectiveness of the proposed approach.