Tracking variable number of targets using sequential Monte Carlo methods

In this paper, we present a simulation-based method for multitarget tracking and detection using sequential Monte Carlo (SMC), or particle filtering (PF) methods. The proposed approach is applicable to nonlinear and non-Gaussian models for the target dynamics and measurement likelihood, where the environment is characterised by high clutter rate and low detection probability. The number of targets is estimated by continuously monitoring the events being represented by the regions of interest (ROIs) in the surveillance region. Subsequent to target detection, the sequential importance sampling filter is employed for recursive target state estimation, in conjunction with a 2-D data assignment method for measurement-to-target association. Computer simulations are also included to demonstrate and evaluate the performance of the proposed approach.