"XPFCP": An Extended Particle Filter for Tracking Multiple and Dynamic Objects in Complex Environments

The work presented in this paper explores a new solution for tracking multiple and dynamic objects in complex environments. An extended particle filter (XPF) is used to implement a multimodal distribution that will represent the most probable estimation for each object position. A standard particle filter (PF) cannot be used with a variable number of obstacles, and some other solutions have been tested in different previous works, but most of them are very expensive in time and memory resources at least for a high number of obstacles to be tracked. The solution exposed here includes a clustering procedure that increases the robustness of the probabilistic process to adapt itself on-line to the variable number of clusters. The presented algorithm has been tested with sonar and stereovision measurements and some results included in the paper show the efficiency of the proposed work.