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

Authors

Evangelos Giannopoulos, Naval Undersea Warfare Center Division Newport
Roy Streit, Naval Undersea Warfare Center Division Newport
Peter Swaszek, Naval Undersea Warfare Center Division Newport

Document Type

Conference Proceeding

Date of Original Version

12-1-1996

Abstract

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

Citation/Publisher Attribution

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