Paper Info
Title
A new all-neighbor fuzzy association technique for multitarget tracking in a cluttered environment.
Abstract
Multitarget tracking in a cluttered environment is a significant problem in a wide variety of applications. A typical approach to deal with such problem is the joint probabilistic data association filter. The joint probabilistic data association filter determines the joint probabilities over all targets and hits and updates the predicted target state estimate using a probability weighted sum of residuals. This paper proposes a new all-neighbor fuzzy association technique. 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 all-neighbor approach determines the similarity measures between measurements and tracks in terms of fuzzy weights. It associates measurements into tracks using fuzzy scores and updates the predicted target state estimate using a fuzzy weighted sum of residuals. The proposed technique performs data association based on a single possibility matrix between measurements and tracks; thus it highly reduces the computational complexity compared to other all-neighbor fuzzy techniques reported in the literature. The proposed technique can be applied to non-maneuvering targets as well as maneuvering targets in a cluttered environment. Its performance is compared to the joint probabilistic data association technique, the nearest-neighbor standard filter, and perfect data association. The results showed the efficiency of the proposed technique.
Year
DOI
Venue
2009
10.1109/FUZZY.2009.5277347
FUZZ-IEEE
Keywords
Field
DocType
possibility theory,joint probabilistic data association filter,noise,fuzzy set theory,probability,clutter,sensor fusion,conditional probability,logic gates,nearest neighbor,computational complexity,noise measurement
Joint probability distribution,Similarity measure,Joint Probabilistic Data Association Filter,Pattern recognition,Conditional probability,Computer science,Fuzzy logic,Possibility theory,Fuzzy set,Artificial intelligence,Probabilistic logic,Machine learning
Conference
ISSN
ISBN
Citations 
1098-7584 E-ISBN : 978-1-4244-3597-5
978-1-4244-3597-5
1
PageRank 
References 
Authors
0.36
6
1
Name
Order
Citations
PageRank
Ashraf M. Aziz1988.72