DocumentCode :
632082
Title :
Distributed data association for multiple-target tracking using game theory
Author :
Chavali, Phani ; Nehorai, Arye
Author_Institution :
Dept. of Electr. & Syst. Eng., Washington Univ. in St. Louis, St. Louis, MO, USA
fYear :
2013
fDate :
April 29 2013-May 3 2013
Firstpage :
1
Lastpage :
6
Abstract :
In this paper, we develop a game-theoretic framework to address data association for multiple-target tracking problems. We model the interaction among trackers as a game, by considering them as players, and the set of measurements as strategies. We develop utility functions for the players, and use a regret-based learning algorithm to find the equilibrium of the game. We will then use Monte Carlo filters, operating in parallel, to track state vectors corresponding to the individual targets. In contrast to the traditional Monte Carlo filters that sample the association vector, we first find the association in a deterministic fashion, and then use Monte Carlo sampling on the reduced dimensional state of each target independently, thereby enabling a distributed implementation. We provide numerical results to demonstrate the performance of our proposed filtering algorithm.
Keywords :
Monte Carlo methods; filtering theory; game theory; learning (artificial intelligence); sensor fusion; target tracking; Monte Carlo filters; Monte Carlo sampling; association vector; distributed data association; game-theoretic framework; multiple-target tracking problems; regret-based learning algorithm; utility functions; Clutter; Games; Monte Carlo methods; Radar tracking; Target tracking; Time measurement; Vectors; correlated-equilibrium; distributed data association; game theory; multi-target tracking; regret matching;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Radar Conference (RADAR), 2013 IEEE
Conference_Location :
Ottawa, ON
ISSN :
1097-5659
Print_ISBN :
978-1-4673-5792-0
Type :
conf
DOI :
10.1109/RADAR.2013.6586129
Filename :
6586129
Link To Document :
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