TRACKING VARIABLE NUMBER OF TARGETS USING SEQUENTIAL MONTE CARLO
METHODS
William Ng, Jack Li, Simon Godsill, and Jaco Vermaak
Cambridge University
Department of Engineering
Cambridge, UK
Emails: fkfn20 ,jfl28, sjg, jv211g@eng.cam.ac.uk
ABSTRACT
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.