Multiresolutional decomposition and modeling with an application to joint probabilistic data association

Authors:
L. Hong;C. Wang;Z. Ding
Affiliations:
Department of Electrical Engineering Wright State University, Dayton, OH 45435, U.S.A.;Department of Electrical Engineering Wright State University, Dayton, OH 45435, U.S.A.;Department of Electrical Engineering Wright State University, Dayton, OH 45435, U.S.A.
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
1997

Citing 4
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Abstract

A multiresolutional approach for measurement decomposition and system modeling is presented in this paper. The decomposition is performed in both spatial and time domains and provides an excellent platform for developing computationally efficient algorithms. Using multiresolutional decomposition and modeling, a multiresolutional joint probabilistic data association (MR-JPDA) algorithm is developed for multiple target tracking. Monte Carlo simulations demonstrate that the computation of the MRJPDA algorithm is much less than the traditional joint probabilistic data association (JPDA) algorithm with a comparable performance.