DocumentCode :
614618
Title :
Likelihood-surface based discretization for tracking via tree search
Author :
Roufarshbaf, Hossein ; Nelson, J.K.
Author_Institution :
Electr. & Comput. Eng., George Mason Univ., Fairfax, VA, USA
fYear :
2013
fDate :
20-22 March 2013
Firstpage :
1
Lastpage :
6
Abstract :
A new discretization technique based on local maxima of the observation likelihood surface is proposed for tree-search based tracking of dim targets in heavy clutter. The joint likelihood of sensor observations over the target state space is evaluated in the vicinity of the previously estimated target state, and its local maxima are selected as new states for discretization. The discretized states are used to build a search tree, which is navigated using the stack algorithm to approximate the maximum a posteriori tracking solution. Simulation results on a benchmark active sonar data set reveal that the proposed algorithm is able to follow dim maneuvering targets without track fragmentation.
Keywords :
maximum likelihood estimation; sensors; target tracking; tree searching; dim maneuvering targets; dim target tracking; discretization technique; likelihood-surface based discretization; local maxima; maximum a posteriori tracking solution; observation likelihood surface; sensor observations; stack algorithm; tree-search based target tracking; Channel models; Clutter; Correlation; Radar tracking; Receivers; Sampling methods; Target tracking; Target tracking; likelihood surface; tree search;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Sciences and Systems (CISS), 2013 47th Annual Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
978-1-4673-5237-6
Electronic_ISBN :
978-1-4673-5238-3
Type :
conf
DOI :
10.1109/CISS.2013.6552306
Filename :
6552306
Link To Document :
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