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 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.
Publication Title, e.g., Journal
ADFS 1996 - Australian Data Fusion Symposium
Giannopoulos, E., R. Streit, and P. Swaszek. "Probabilistic multi-hypothesis tracking in a multi-sensor, multi-target environment." ADFS 1996 - Australian Data Fusion Symposium (1996): 184-189. doi: 10.1109/ADFS.1996.581104.