An Accelerated IMM JPDA Algorithm for Tracking Multiple Manoeuvring Targets in Clutter

Abstract

Theoretically the most powerful approach for tracking multiple targets is known to be Multiple Hypothesis Tracking (MHT) method. The MHT method, however, leads to combinatorial explosion and computational overload. By using an algorithm for finding the K-best assignments, MHT approach can be considerably optimized in terms of computational load. A much simpler alternative of MHT approach can be the Joint Probabilistic Data Association (JPDA) algorithm combined with Interacting Multiple Models (IMM) approach. Even though it is much simpler, this approach can overwhelm computations as well. To overcome this drawback an algorithm due to Murty and optimized by Miller, Stone and Cox is embedded in IMM-JPDA algorithm for determining a ranked set of K-best hypotheses instead of all feasible hypotheses. The presented algorithm assures continuous maneuver detection and adequate estimation of manoeuvring targets in heavy clutter. This affects in a good target tracking performance with limited computational and memory requirements. The corresponding numerical results are presented.