Three-dimensional axial assignment problems with decomposable cost coefficients
Discrete Applied Mathematics - Special volume: first international colloquium on graphs and optimization (GOI), 1992
Approximation algorithms for NP-hard problems
Approximation algorithms for NP-hard problems
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Approximating discrete collections via local improvements
Proceedings of the sixth annual ACM-SIAM symposium on Discrete algorithms
Distributed sensor network for real time tracking
Proceedings of the fifth international conference on Autonomous agents
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Angle Optimization in Target Tracking
SWAT '08 Proceedings of the 11th Scandinavian workshop on Algorithm Theory
Can you see me now? sensor positioning for automated and persistent surveillance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The focus of attention problem
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Local search heuristics for the multidimensional assignment problem
Journal of Heuristics
Video surveillance with PTZ cameras: the problem of maximizing effective monitoring time
ICDCN'10 Proceedings of the 11th international conference on Distributed computing and networking
Coverage quality and smoothness criteria for online view selection in a multi-camera network
ACM Transactions on Sensor Networks (TOSN)
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In this paper, we consider the problem of assigning sensors to track targets so as to minimize the expected error in the resulting estimation for target locations. Specifically, we are interested in how disjoint pairs of bearing or range sensors can be best assigned to targets to minimize the expected error in the estimates. We refer to this as the focus of attention (FOA) problem. In its general form, FOA is NP-hard and not well approximable. However, for specific geometries we obtain significant approximation results: a 2-approximation algorithm for stereo cameras on a line, a (1+@e)-approximation algorithm for any constant @e when the cameras are equidistant, and a 1.42-approximation algorithm for equally spaced range sensors on a circle. In addition to constrained geometries, we further investigate the problem for general sensor placement. By reposing as a maximization problem-where the goal is to maximize the number of tracks with bounded error-we are able to leverage results from maximum set-packing to render the problem approximable. We demonstrate the utility of these algorithms in simulation for a target tracking task, and for localizing a team of mobile agents in a sensor network. These results provide insights into sensor/target assignment strategies, as well as sensor placement in a distributed network.