The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking
Automatica (Journal of IFAC)
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In this paper, we present an online approach for joint initiation/termination and tracking for multiple targets with multiple sensors using sequential Monte Carlo (SMC) methods. There are several main contributions in the paper. The first contribution is the extension of the deterministic initiation and termination method proposed by the authors' previous publications to a full SMC context in which track initiation/termination are executed with sampling methods. In effect, the dimensions of the particles are variable. In addition, we also integrate a Markov random field (MRF) motion model with the framework to enable efficient and accurate tracking for interacting targets and to avoid potential track coalescence problems. With the employment of multiple sensors, a centralized tracking strategy is adopted, where the observations from all active sensors are fused together for target initiation/termination and tracking and a set of global tracks is maintained. Intra-and inter-sensor clusters are constructed, comprised of closely spaced observations either in time for single sensors or from distinct sensors at a single time, that can increase the reliability when proposing new tracks for initiation. Computer simulations demonstrate that the proposed approach is robust in joint initiation/termination and tracking of multiple manoeuvring targets even when the environment is hostile with high-clutter rates and low target detection probabilities. The integration of the MRF framework into the proposed methods improves robustness in handling close target interactions when the observation noise is high.