A Fixed Lag IPDA Smoothing for Target Tracking in Clutter

Abstract

This paper presents a fixed lag smoothing algorithm for target tracking in clutter. The proposed algorithm is based on integrated probabilistic data association (IPDA) approach. The algorithm runs two filters for each track-one in forward direction (as standard IPDA) and the other in backward direction. A standard fusion of both the target existence probability and state estimates of both the filters at a fixed time lag yields the smoothed probability densities of both of them. The paper also presents the refined target dynamics and target existence transition model for backward running IPDA filter. Simulation results are also presented to compare the performance (in terms of true track detection, false track discrimination) of the proposed IPDA smoother with that of augmented state IPDA filter and standard IPDA filter