Probabilistic multi-hypothesis tracking in a multi-sensor, multi-target environment

Abstract

In this paper the probabilistic multi-hypothesis tracking (PMHT) algorithm, a data fusion algorithm developed by Streit and Luginbuhl (1995), is extended to handle multiple sensors. In addition, performance of multi-target tracking algorithms is discussed in terms of the Cramer-Rao lower bound (CRLB) criterion that is computed from the marginalized measurement PMHT log-likelihood function. Simulation results for one set of scenarios are presented and an initialization procedure for the bearings only measurement case is recommended.