Sensor management using an active sensing approach
Signal Processing
Active learning for Hidden Markov Models: objective functions and algorithms
ICML '05 Proceedings of the 22nd international conference on Machine learning
Nonmyopic sensor scheduling and its efficient implementation for target tracking applications
EURASIP Journal on Applied Signal Processing
Optimal Threshold Policies for Multivariate Stopping-Time POMDPs
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Approximate stochastic dynamic programming for sensor scheduling to track multiple targets
Digital Signal Processing
Sensor selection for active information fusion
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Optimal threshold policies for multivariate POMDPs in radar resource management
IEEE Transactions on Signal Processing
IEEE Transactions on Wireless Communications
Efficient sensor selection for active information fusion
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
A time-varying opportunistic approach to lifetime maximization of wireless sensor networks
IEEE Transactions on Signal Processing
Journal of Real-Time Image Processing
On the Network Coverage Intensity in the Presence of Clock Asynchrony
Wireless Personal Communications: An International Journal
In-situ soil moisture sensing: measurement scheduling and estimation using compressive sensing
Proceedings of the 11th international conference on Information Processing in Sensor Networks
Automatica (Journal of IFAC)
Ultra wideband indoor positioning system in support of emergency evacuation
Proceedings of the Fifth ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness
Scheduling sensors for monitoring sentient spaces using an approximate POMDP policy
Pervasive and Mobile Computing
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The author considers a hidden Markov model (HMM) where a single Markov chain is observed by a number of noisy sensors. Due to computational or communication constraints, at each time instant, one can select only one of the noisy sensors. The sensor scheduling problem involves designing algorithms for choosing dynamically at each time instant which sensor to select to provide the next measurement. Each measurement has an associated measurement cost. The problem is to select an optimal measurement scheduling policy to minimize a cost function of estimation errors and measurement costs. The optimal measurement policy is solved via stochastic dynamic programming. Sensor management issues and suboptimal scheduling algorithms are also presented. A numerical example that deals with the aircraft identification problem is presented