Probabilistic multi-hypothesis tracking in a multi-sensor, multi-target environment
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.
Proceedings of the Australian Data Fusion Symposium
Giannopoulos, Evangelos, Roy Streit, and Peter Swaszek. "Probabilistic multi-hypothesis tracking in a multi-sensor, multi-target environment." Proceedings of the Australian Data Fusion Symposium , (1996): 184-189. https://digitalcommons.uri.edu/ele_facpubs/1099