Sequential Monte Carlo methods for joint detection and tracking of multiaspect targets in infrared radar images

Abstract

We present in this paper a sequential Monte Carlo methodology for joint detection and tracking of a multiaspecttarget in image sequences. Unlike the traditional contact/association approach found in the literature, theproposed methodology enables integrated, multiframe target detection and tracking incorporating the statisticalmodels for target aspect, target motion, and background clutter. Two implementations of the proposed algorithmare discussed using, respectively, a resample-move (RS) particle filter and an auxiliary particle filter (APF). Oursimulation results suggest that the APF configuration outperforms slightly the RS filter in scenarios of stealthytargets.