Distributed interacting multipattern data association for multiplatform target tracking

Authors:
Lang Hong
Affiliations:
Department of Electrical Engineering, 3640 Colonel Glenn Hwy, Wright State University, Dayton
Venue:
Signal Processing
Year:
2002

Citing 3
Cited 1

Real-time optimal filtering for stochastic systems with multiresolutional measurements

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An interacting multipattern data association (IMPDA) tracking algorithm

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An interacting multipattern joint probabilistic data association (IMP-JPDA) algorithm for multitarget tracking

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Distributed interacting multiple model H∞ filtering fusion for multiplatform maneuvering target tracking in clutter

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Abstract

A distributed interacting multipattern data association tracking algorithm is developed in this paper. Multipattern data association is based on a novel approach for tracking multiple targets using multiple patterns extracted from measurement sequences. A Markov chain controls the switching behavior of multipatterns, which allows an interacting operation of multipatterns. To save the communication bandwidth, the distributed interacting multipattern data association tracking algorithm is based on exchanges of combined target tracks, which creates a unique difficulty in developing an optimal fusion algorithm. The difficulty is overcome by developing equivalent platform and global models which then propagate the fused tracks needed for the distributed fusion algorithm.