Title :
Efficient methods of non-myopic sensor management for multitarget tracking
Author :
Kreucher, Chris ; Hero, Alfred O., III ; Kastella, Keith ; Chang, Dan
Author_Institution :
Dept. of EECS, Michigan Univ., Ann Arbor, MI, USA
Abstract :
This paper develops two efficient methods of long-term sensor management and investigates the benefit in the setting of multitarget tracking. The underlying tracking methodology is based on recursive estimation of a joint multitarget probability density (JMPD), implemented via particle filtering methods. The myopic sensor management scheme is based on maximizing the expected Renyi divergence between the JMPD and the JMPD after a new measurement is made. Since a full non-myopic solution is computationally intractable when looking more than a small number of time steps ahead, two approximate strategies are investigated. First, we develop an information-directed search which focuses Monte Carlo evaluations on action sequences that are most informative. Second, we give an approximate method of solving Bellman´s equation which replaces the value-to-go with an easily computed function that approximates the long term value of the action. The performance of these methods is compared in terms of tracking performance and computational requirements.
Keywords :
Monte Carlo methods; optimisation; probability; sensor fusion; target tracking; Bellman equation; Monte Carlo method; Renyi divergence; joint multitarget probability density; multitarget tracking; myopic sensor management scheme; particle filtering methods; recursive estimation; Bayesian methods; Contracts; Equations; Filtering; Kinematics; Monte Carlo methods; Particle tracking; Processor scheduling; Recursive estimation; Target tracking;
Conference_Titel :
Decision and Control, 2004. CDC. 43rd IEEE Conference on
Conference_Location :
Nassau
Print_ISBN :
0-7803-8682-5
DOI :
10.1109/CDC.2004.1428735