Fuzzy multisensor tracking system

Chun Chung Chan, Vika Lee, Henry Leung

Research output: Journal article publicationConference articleAcademic researchpeer-review

2 Citations (Scopus)


Many multi-sensor target tracking systems are developed under the assumptions that data association is too complex and computational requirement is too excessive for centralized fusion approaches to be practical. In addition, it is also assumed that the noise component is relatively small, that there are no missed detection and that the scanning interval is relatively short, etc. Many multi-sensor tracking systems have been shown to be able to perform effectively when tested with simulated data generated under these assumptions. However, careful investigation into the characteristics of several sets of real data reveals that these assumptions cannot always be made validly. In this paper, we first describe the characteristics of a real multisensor tracking environment and explain why existing systems may not be able to perform their task effectively in such environment. We then present a data fusion technique that can overcome some of the weaknesses of these systems. This technique consists of three steps: (i) estimation of synchronization error using an adaptive learning approach; (ii) adjustment of measured positions of a target in case of missed detection; and (iii) prediction of the next target position using a fuzzy logic based algorithm. For performance evaluation, we tested the technique using different sets of real and simulated data. The results obtained are very satisfactory.
Original languageEnglish
Pages (from-to)279-287
Number of pages9
JournalProceedings of SPIE - The International Society for Optical Engineering
Publication statusPublished - 1 Jan 1999
EventProceedings of the 1999 Sensor Fusion: Architectures, Algorithms, and Applications III - Orlando, FL, United States
Duration: 7 Apr 19999 Apr 1999

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics


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