Digital Control of Dynamic Systems
Digital Control of Dynamic Systems
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Approximate distributed Kalman filtering in sensor networks with quantifiable performance
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Estimation in sensor networks: a graph approach
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Block matrices with L-block-banded inverse: inversion algorithms
IEEE Transactions on Signal Processing
Analytic alpha-stable noise modeling in a Poisson field ofinterferers or scatterers
IEEE Transactions on Signal Processing
Distributing the Kalman Filter for Large-Scale Systems
IEEE Transactions on Signal Processing - Part I
Data assimilation in large time-varying multidimensional fields
IEEE Transactions on Image Processing
Modeling of future cyber-physical energy systems for distributed sensing and control
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Signal recovery with cost-constrained measurements
IEEE Transactions on Signal Processing
Hi-index | 35.69 |
This paper concerns the problem of estimating a spatially distributed, time-varying random field from noisy measurements collected by a wireless sensor network. When the field dynamics are described by a linear, lumped-parameter model, the classical solution is the Kalman-Bucy filter (KBF). Bandwidth and energy constraints can make it impractical to use all sensors to estimate the field at specific locations. Using graph-theoretic techniques, we show how reduced-order KBFs can be constructed that use only a subset of the sensors, thereby reducing energy consumption. This can lead to degraded performance, however, in terms of the root mean squared (RMS) estimation error. Efficient methods are presented to apply Pareto optimalIty to evaluate the tradeoffs between communication costs and RMS estimation error to select the best reduced-order KBF. The approach is illustrated with simulation results.