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Proceedings Paper

Fuzzy logic extended rule set for multitarget tracking
Author(s): Michael J. Horton; Richard A. Jones
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Paper Abstract

An extended rule set multitarget tracking system based on fuzzy logic is discussed. The system that we describe utilizes fuzzy logic principles in a Kalman-type fuzzy tracking filter for the purpose of drastically reducing computation overhead. The tracking problem is modelled as a discrete time system in the standard state form. Rather that employing the Kalman filter, an extended rule set is used at each iteration to update an estimate of the target state vector. The update is based upon the innovation vector for the target, which is the vector difference between observed and estimated target position. Since each target has many returns, both actual and clutter, it is necessary to validate each return before including it in the calculation of the average innovation vector. Return validation is accomplished via fuzzy logic and is incorporated with a fuzzy measure of target-return similarity in the fuzzy return processor (FRP) to obtain the average innovation vector. Spurious returns are discussed in terms of their causes and their effect on the fuzzy tracking algorithm. The rule set is described in detail, and the results are discussed and compared to Kalman filter tracking results. Finally, the algorithm is applied to an actual forward-looking infrared (FLIR) image sequence provided by Texas Instruments, Inc. and results are discussed accordingly.

Paper Details

Date Published: 26 May 1995
PDF: 12 pages
Proc. SPIE 2468, Acquisition, Tracking, and Pointing IX, (26 May 1995); doi: 10.1117/12.210422
Show Author Affiliations
Michael J. Horton, Harris Corp. (United States)
Richard A. Jones, Univ. of Arkansas (United States)


Published in SPIE Proceedings Vol. 2468:
Acquisition, Tracking, and Pointing IX
Michael K. Masten; Larry A. Stockum, Editor(s)

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