Abstract
We present in this paper a sequential Monte Carlo methodology for joint detection and tracking of a multiaspect target in image sequences. Unlike the traditional contact/association approach found in the literature, the proposed methodology enables integrated, multiframe target detection and tracking incorporating the statistical models for target aspect, target motion, and background clutter. Two implementations of the proposed algorithm are discussed using, respectively, a resample-move (RS) particle filter and an auxiliary particle filter (APF). Our simulation results suggest that the APF configuration outperforms slightly the RS filter in scenarios of stealthy targets.
Highlights
This paper investigates the use of sequential Monte Carlo filters [1] for joint multiframe detection and tracking of randomly changing multiaspect targets in a sequence of heavily cluttered remote sensing images generated by an infrared airborne radar (IRAR) [2]
We proposed in [4] an alternative tracker based on particle filtering [1, 8] which, contrary to the original hidden Markov model (HMM) tracker in [7], assumed a continuous-valued kinematic state and a discrete-valued target aspect state
We quantify the performance of the proposed sequential Monte Carlo detector/tracker, both in the RS and auxiliary particle filter (APF) configurations, using simulated infrared airborne radar (IRAR) data
Summary
This paper investigates the use of sequential Monte Carlo filters [1] for joint multiframe detection and tracking of randomly changing multiaspect targets in a sequence of heavily cluttered remote sensing images generated by an infrared airborne radar (IRAR) [2]. The pixel locations where the output of the matched filter exceeds a pre-specified threshold are treated as initial estimates of the true position of detected targets. Those preliminary position estimates are subsequently assimilated into a multiframe tracking algorithm, usually a linearized Kalman filter, or alternatively discarded as false alarms originating from clutter
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