Multiple extended target tracking based on distributed multi-sensor fusion and shape estimation

Abstract

Distributed multi-sensor fusion based on the generalised covariance intersection (GCI) fusion has been widely integrated into the Random Finite Set theory, which is promising for multi-target tracking with an unknown number of targets. However, it has not been widely investigated in the multiple extend target tracking (METT) field, and there is still an open problem on how to solve the inconsistency of label space among the sensors. For these problems, we first introduce the GCI fusion into the METT and proposed an association algorithm by considering the estimated shapes and the target positions to avoid the phenomenon of the label inconsistency as well as to reduce the computational burden. Simulation results show that the proposed algorithm has a better tracking performance than the traditional METT algorithms.