Brief paper: Optimal linear estimation for systems with multiple packet dropouts
Automatica (Journal of IFAC)
H∞ filtering of networked discrete-time systems with random packet losses
Information Sciences: an International Journal
Power-efficient dimensionality reduction for distributed channel-aware kalman tracking using WSNs
IEEE Transactions on Signal Processing
Distributed estimation in energy-constrained wireless sensor networks
IEEE Transactions on Signal Processing
Brief paper: Multi-rate stochastic H∞ filtering for networked multi-sensor fusion
Automatica (Journal of IFAC)
Distributed Estimation and Detection for Sensor Networks Using Hidden Markov Random Field Models
IEEE Transactions on Signal Processing
Distributed Estimation Using Reduced-Dimensionality Sensor Observations
IEEE Transactions on Signal Processing
Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals
IEEE Transactions on Signal Processing
Gossip and Distributed Kalman Filtering: Weak Consensus Under Weak Detectability
IEEE Transactions on Signal Processing
Power scheduling of universal decentralized estimation in sensor networks
IEEE Transactions on Signal Processing
Decentralized Quantized Kalman Filtering With Scalable Communication Cost
IEEE Transactions on Signal Processing - Part I
Multi-sensor optimal information fusion Kalman filter
Automatica (Journal of IFAC)
Toward a theory of in-network computation in wireless sensor networks
IEEE Communications Magazine
Distributed Kalman filtering based on consensus strategies
IEEE Journal on Selected Areas in Communications
Hi-index | 22.14 |
This paper presents a distributed fusion estimation method for estimating states of a dynamical process observed by wireless sensor networks (WSNs) with random packet losses. It is assumed that the dynamical process is not changing too rapidly, and a multi-rate scheme by which the sensors estimate states at a faster time scale and exchange information with neighbors at a slower time scale is proposed to reduce communication costs. The estimation is performed by taking into account the random packet losses in two stages. At the first stage, every sensor in the WSN collects measurements from its neighbors to generate a local estimate, then local estimates in the neighbors are further collected at the second stage to form a fused estimate to improve estimation performance and reduce disagreements among local estimates at different sensors. Local optimal linear estimators are designed by using the orthogonal projection principle, and the fusion estimators are designed by using a fusion rule weighted by matrices in the linear minimum variance sense. Simulations of a target tracking system are given to show that the time scale of information exchange among sensors can be slower while still maintaining satisfactory estimation performance by using the developed estimation method.