Sharif Digital Repository

In general, multi-target tracking consists of estimation of the posterior density function of present targets at each scan in the observation area. These targets may have unknown and time varying number of targets. It is a tough job due to misdetections, false alarms, data association ambiguity, and nonlinear equations-non Gaussian noises. These all make it difficult to apply Kalman filter and its extensions such as extended Kalman filter and unscented Kalman filter. Monte Carlo methods, particularly particle filters, have recently aroused the interest of designers and enjoyed a lot of success to deal with multi-target tracking difficulties. In addition, they can handle nonthresholded data...