Title
Nonmyopic sensor scheduling and its efficient implementation for target tracking applications
Abstract
We propose two nonmyopic sensor scheduling algorithms for target tracking applications. We consider a scenario where a bearing-only sensor is constrained to move in a finite number of directions to track a target in a two-dimensional plane. Both algorithms provide the best sensor sequence by minimizing a predicted expected scheduler cost over a finite time-horizon. The first algorithm approximately computes the scheduler costs based on the predicted covariance matrix of the tracker error. The second algorithm uses the unscented transform in conjunction with a particle filter to approximate covariance-based costs or information-theoretic costs. We also propose the use of two branch-and-bound-based optimal pruning algorithms for efficient implementation of the scheduling algorithms. We design the first pruning algorithm by combining branch-and-bound with a breadth-first search and a greedy-search; the second pruning algorithm combines branch-and-bound with a uniform-cost search. Simulation results demonstrate the advantage of nonmyopic scheduling over myopic scheduling and the significant savings in computational and memory resources when using the pruning algorithms.
Year
DOI
Venue
2006
10.1155/ASP/2006/31520
EURASIP J. Adv. Sig. Proc.
Keywords
Field
DocType
branch-and-bound-based optimal pruning algorithm,scheduler cost,scheduling algorithm,efficient implementation,nonmyopic sensor scheduling,pruning algorithm,nonmyopic sensor scheduling algorithm,sensor sequence,breadth-first search,bearing-only sensor,nonmyopic scheduling,myopic scheduling
Information theory,Mathematical optimization,Finite set,Scheduling (computing),Computer science,Particle filter,Algorithm,Unscented transform,Covariance matrix,Pruning,Covariance
Journal
Volume
Issue
ISSN
2006,
1
1687-6180
Citations 
PageRank 
References 
15
1.38
11
Authors
3
Name
Order
Citations
PageRank
Amit S. Chhetri1232.59
Darryl Morrell215917.45
Antonia Papandreou-Suppappola323429.88