Automatic track initialization and maintenance in heavy clutter using integrated JPDA and ML-PDA algorithms

Abstract

Target tracking in high clutter or low signal-to-noise environments presents many challenges to tracking systems. Joint Maximum Likelihood estimator combined with Probabilistic Data Association (JML-PDA) is a well-known parameter estimation solution for the initialization of tracks of very low observable and low signal-to-noise-ratio targets in higher clutter environments. On the other hand, the Joint Probabilistic Data Association (JPDA) algorithm, which is commonly used for track maintenance, lacks automatic track initialization capability. This paper presents an algorithm to automatically initialize and maintain tracks using an integrated JPDA and JML-PDA approach that seamlessly shares information on existing tracks between the JML-PDA (used for initialization) and JPDA (used for maintenance) components. The motivation is to share information between the maintenance and initialization stages of the tracker, that are always on-going, so as to enable the tracking of an unknown number of targets using the JPDA approach in heavy clutter. The effectiveness of the new algorithm is demonstrated on a heavy clutter scenario and its performance is tested on negibouring targets with association ambiguity using angle-only measurements.