On the representation and estimation of spatial uncertainly
International Journal of Robotics Research
Vector quantization and signal compression
Vector quantization and signal compression
Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems
Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Stability of Kalman filtering with Markovian packet losses
Automatica (Journal of IFAC)
Tree Approximations of Dynamic Stochastic Programs
SIAM Journal on Optimization
Sensor selection via convex optimization
IEEE Transactions on Signal Processing
Quantization of filter bank frame expansions through moving horizon optimization
IEEE Transactions on Signal Processing
Linear Coherent Decentralized Estimation
IEEE Transactions on Signal Processing
Moving horizon design of discrete coefficient FIR filters
IEEE Transactions on Signal Processing
A survey on power control issues in wireless sensor networks
IEEE Communications Surveys & Tutorials
Wireless sensor networking [Guest Editorial]
IEEE Wireless Communications
Survey Constrained model predictive control: Stability and optimality
Automatica (Journal of IFAC)
Source coding and graph entropies
IEEE Transactions on Information Theory
On zero-error coding of correlated sources
IEEE Transactions on Information Theory
Brief paper: Data-driven communication for state estimation with sensor networks
Automatica (Journal of IFAC)
On Kalman filtering over fading wireless channels with controlled transmission powers
Automatica (Journal of IFAC)
Adaptive controller placement for wireless sensor-actuator networks with erasure channels
Automatica (Journal of IFAC)
| Hi-index | 35.69 |
We study state estimation via wireless sensors over fading channels. Packet loss probabilities depend upon time-varying channel gains, packet lengths and transmission power levels of the sensors. Measurements are coded into packets by using either independent coding or distributed zero-error coding. At the gateway, a time-varying Kalman filter uses the received packets to provide the state estimates. To trade sensor energy expenditure for state estimation accuracy, we develop a predictive control algorithm which, in an online fashion, determines the transmission power levels and codebooks to be used by the sensors. To further conserve sensor energy, the controller is located at the gateway and sends coarsely quantized power increment commands, only whenever deemed necessary. Simulations based on real channel measurements illustrate that the proposed method gives excellent results.