A novel all-neighbor fuzzy association approach for multitarget tracking in a cluttered environment

This paper proposes a novel all-neighbor fuzzy association approach for multitarget tracking in a cluttered environment. It performs data association with a little prior knowledge and updates the predicted target state estimate using a fuzzy weighted sum of innovations. Unlike the joint probabilisti...

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Veröffentlicht in:Signal processing Jg. 91; H. 8; S. 2001 - 2015
1. Verfasser: Aziz, Ashraf M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.08.2011
Elsevier
Schlagworte:
Algorithms
Applied sciences
Data association
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Fuzzy
Fuzzy logic
Fuzzy set theory
Information theory
Information, signal and communications theory
Joint probabilistic data association
Maneuvering targets
Multitarget tracking
Probabilistic methods
Probability theory
Signal and communications theory
Signal representation. Spectral analysis
Signal, noise
Similarity
Telecommunications and information theory
Tracking
Tracking in a clutter
Multitarget tracking
Joint probabilistic data association
Maneuvering targets
Tracking in a clutter
Data association
Performance evaluation
Automatic classification
Moving target
Similarity
Updating
Multiple target
Conditional probability
Clutter
Innovation
Fuzzy algorithm
State estimation
Target tracking
Probabilistic approach
Computer simulation
Signal classification
Computational complexity
Information processing
Signal processing
Data fusion
Feasibility
ISSN:0165-1684, 1872-7557
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:This paper proposes a novel all-neighbor fuzzy association approach for multitarget tracking in a cluttered environment. It performs data association with a little prior knowledge and updates the predicted target state estimate using a fuzzy weighted sum of innovations. Unlike the joint probabilistic data association filter, in which the similarity measures are determined in terms of the conditional probability for all feasible data association hypothesis, the proposed fuzzy association approach determines the similarity measures between measurements and tracks in terms of possibility weights based on a partition matrix. The possibility weights are determined according to the fuzzy clustering algorithm. The proposed approach is able to perform all-neighbor association with a lower computational complexity in the expense of a little lower performance compared to the standard joint probabilistic data association filter. Computer simulation shows the feasibility and the efficiency of the proposed all-neighbor fuzzy association approach.
Bibliographie:ObjectType-Article-2
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content type line 23
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2011.03.007