DocumentCode :
567511
Title :
On mixture reduction for multiple target tracking
Author :
Ardeshiri, Tohid ; Orguner, Umut ; Lundquist, Christian ; Schön, Thomas B.
Author_Institution :
Dept. of Electr. Eng., Linkoping Univ., Linkoping, Sweden
fYear :
2012
fDate :
9-12 July 2012
Firstpage :
692
Lastpage :
699
Abstract :
In multiple hypothesis or probability hypothesis based multiple target tracking the resulting mixtures with ever growing components should be approximated by a reduced mixture. Although there are cost based and more rigorous mixture reduction algorithms, which are computationally expensive to apply in practical situations especially in high dimensional state spaces, the mixture reduction is generally done based on ad hoc criteria and procedures. In this paper we propose a sequentially pairwise mixture reduction criterion and algorithm based on statistical decision theory. For this purpose, we choose the merging criterion for the mixture components based on a likelihood ratio test. The advantages and disadvantages of some of the previous reduction schemes and the newly proposed algorithm are discussed in detail. The results are evaluated on a Gaussian mixture implementation of the PHD filter where two different pruning and merging schemes are designed: one for computational feasibility, the other for the state extraction.
Keywords :
Gaussian processes; decision theory; feature extraction; filtering theory; probability; statistical testing; target tracking; Gaussian mixture implementation; PHD filter; ad hoc criteria; high dimensional state spaces; likelihood ratio test; mixture reduction algorithms; multiple hypothesis; multiple target tracking; probability hypothesis; pruning-merging schemes; sequentially pairwise mixture reduction criterion; state extraction; statistical decision theory; Algorithm design and analysis; Approximation algorithms; Clustering algorithms; Decision theory; Merging; Target tracking; Gaussian Mixture; Merging; Mixture Reduction; PHD Filter; Pruning; Tracking;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Fusion (FUSION), 2012 15th International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4673-0417-7
Electronic_ISBN :
978-0-9824438-4-2
Type :
conf
Filename :
6289870
Link To Document :
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