Multisensor tracking by cooperative processors
Eduardo F. Mallaina, Bruno Cernuschi-Frías · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
Exploiting a new distributed cooperative processing scheme where multiple processors cooperate in finding a global minimum, we have developed a new efficient maximum likelihood (ML) based calculation method for multitarget motion analysis under a fixed networked multisensor environment. The Track estimation of targets from sensor is a crucial issue in active dynamic scene understanding. Multitarget motion analysis, where there are multiple moving targets and multiple fixed sensors which only measure bearings of the targets, is to associate targets and sensor data, and estimate target tracks based on that association. This is NP-hard problem to obtain the optimal solution, as the method easily gets trapped in one of local optima. We applied the decentralized cooperative search technique to this problem, and proved our method effective. The method uses more than one processor, each of which has its own partial search space, searching multiple possibilities in parallel. This paper shows the current status of our research, and presents two prototypes of cooperative multi-agent systems for extended multi-target motion analysis.