Particle filtering for multi-target tracking using jump Markov systems
In multi-target tracking, one jointly estimates the number of targets and the individual target states from sensor measurements. This is a challenging problem due to the time varying number of targets and unknown measurement to target associations. We present a particle filtering method for multi-target tracking. The proposed method applies particle filtering techniques to a jump Markov system that models the multi-target dynamics. Simulation results using this particle method are also presented.