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

In this paper the Probabilistic Multi-Hypothesis Tracking (PMHT) Algorithm, a data fusion algorithm recently developed by Streit and Luginbuhl [1, 2], 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