Stochastic systems: estimation, identification and adaptive control
Stochastic systems: estimation, identification and adaptive control
Exploring artificial intelligence in the new millennium
Entropy-based sensor selection heuristic for target localization
Proceedings of the 3rd international symposium on Information processing in sensor networks
Are GSM Phones THE Solution for Localization?
WMCSA '06 Proceedings of the Seventh IEEE Workshop on Mobile Computing Systems & Applications
Sensor Scheduling for Optimal Observability Using Estimation Entropy
PERCOMW '07 Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications Workshops
Approximate stochastic dynamic programming for sensor scheduling to track multiple targets
Digital Signal Processing
Sensor selection via convex optimization
IEEE Transactions on Signal Processing
Planning-based prediction for pedestrians
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Maximum mutual information principle for dynamic sensor query problems
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
On the Use of Binary Programming for Sensor Scheduling
IEEE Transactions on Signal Processing
Control of systems integrating logic, dynamics, and constraints
Automatica (Journal of IFAC)
Dynamic programming for constrained optimal control of discrete-time linear hybrid systems
Automatica (Journal of IFAC)
Optimal sensor placement and motion coordination for target tracking
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Hi-index | 22.14 |
Consider a set of sensors estimating the state of a process in which only one of these sensors can operate at each time-step due to constraints on the overall system. The problem addressed here is to choose which sensor should operate at each time-step to minimize a weighted function of the error covariances of the state estimates. This work investigates the development of tractable algorithms to solve for the optimal and suboptimal sensor schedules. A condition on the non-optimality of an initialization of the schedule is developed. Using this condition, both an optimal and a suboptimal algorithm are devised to prune the search tree of all possible sensor schedules. The suboptimal algorithm trades off the quality of the solution and the complexity of the problem through a tuning parameter. The performance of the suboptimal algorithm is also investigated and an analytical error bound is provided. Numerical simulations are conducted to demonstrate the performance of the proposed algorithms, and the application of the algorithms in active robotic mapping is explored.