Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Adaptive Markov Control Processes
Adaptive Markov Control Processes
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Rollout Algorithms for Stochastic Scheduling Problems
Journal of Heuristics
Multi-target sensor management using alpha-divergence measures
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Algorithms for optimal scheduling and management of hidden Markovmodel sensors
IEEE Transactions on Signal Processing
A POMDP framework for coordinated guidance of autonomous UAVs for multitarget tracking
EURASIP Journal on Advances in Signal Processing - Special issue on signal processing advances in robots and autonomy
Partially Observable Markov Decision Process Approximations for Adaptive Sensing
Discrete Event Dynamic Systems
Coordinated guidance of autonomous UAVs via nominal belief-state optimization
ACC'09 Proceedings of the 2009 conference on American Control Conference
In-situ soil moisture sensing: measurement scheduling and estimation using compressive sensing
Proceedings of the 11th international conference on Information Processing in Sensor Networks
On efficient sensor scheduling for linear dynamical systems
Automatica (Journal of IFAC)
Planning for multiple measurement channels in a continuous-state POMDP
Annals of Mathematics and Artificial Intelligence
Axis rotation MTD algorithm for weak target detection
Digital Signal Processing
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The problem of sensor scheduling is to select the number and combination of sensors to activate over time. The goal is usually to trade off tracking performance and sensor usage. We formulate a version of this problem involving multiple targets as a partially observable Markov decision process, and use this formulation to develop a nonmyopic sensor-scheduling scheme. Our scheme integrates sequential multisensor joint probabilistic data association and particle filtering for belief-state estimation, and use a simulation-based Q-value approximation method called completely observable rollout for decision making. We illustrate the effectiveness of our approach by an example with multiple sensors activated simultaneously to track multiple targets. We also explore the trade-off between tracking error and sensor cost using our nonmyopic scheme.