A comparative study of Monte-Carlo methods for multitarget tracking

In this paper, we address the problem of tracking an unknown and time varying number of targets and their states from noisy observations available at discrete intervals of time. Attention has recently focused on the role of simulation-based approaches, including Monte Carlo methods, in solving multitarget tracking problem, as these methods are able to perform well for nonlinear and non-Gaussian data models. In this paper, we present a comparative study of several Monte-Carlo methods in terms of estimation quality and complexity.