Paper
5 January 2004 Road-constrained target tracking and identification a particle filter
Craig S Agate, Kevin J. Sullivan
Author Affiliations +
Abstract
Sequential Monte Carlo methods have attracted the attention of the tracking community as a solution to Bayesian estimation particularly for nonlinear problems. Several attributes of particle filters support their use in jointly tracking and identifying ground targets in a road-constrained network. First, since the target dynamics are simulated, propagating a target within a constrained state space is handled quite naturally since the particle filter is not restricted to propagating Gaussian PDFs. Furthermore, a particle filter can approximate a PDF which is a mixture of continuous random variables (the target kinematic state) and discrete random variables (the target ID) which is necessary for the joint tracking and identification problem. Given HRRGMTI measurements of a target, we propose to jointly estimate a target's kinematic state and identification by propagating the joint PDF of the target kinematic state (position and velocity) and target ID. In this way, we capitalize on the inherent coupling between the target's feature measurement (the HRR profile) and the target's kinematic state. In addition to the coupling between a target's feature measurement and the target's kinematic state, there exists a coupling between a target's dynamics and the target's ID which can also be exploited through particle filtering methods. We develop the particle filtering algorithm for tracking and identifying ground targets in a road-constrained environment and present simulation results for a two-class problem.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Craig S Agate and Kevin J. Sullivan "Road-constrained target tracking and identification a particle filter", Proc. SPIE 5204, Signal and Data Processing of Small Targets 2003, (5 January 2004); https://doi.org/10.1117/12.506135
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Cited by 38 scholarly publications.
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KEYWORDS
Particles

Particle filters

Roads

Detection and tracking algorithms

Kinematics

Electronic filtering

Monte Carlo methods

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