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

Document Type

Conference Proceeding

Date of Original Version



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.

Publication Title, e.g., Journal

Proceedings of the Australian Data Fusion Symposium

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