Optimal Filters with Multiple Packet Losses and its Application in Wireless Sensor Networks

This paper is concerned with the filtering problem for both discrete-time stochastic linear (DTSL) systems and discrete-time stochastic nonlinear (DTSN) systems. In DTSL systems, an linear optimal filter with multiple packet losses is designed based on the orthogonal principle analysis approach over unreliable wireless sensor networks (WSNs), and the experience result verifies feasibility and effectiveness of the proposed linear filter; in DTSN systems, an extended minimum variance filter with multiple packet losses is derived, and the filter is extended to the nonlinear case by the first order Taylor series approximation, which is successfully applied to unreliable WSNs. An application example is given and the corresponding simulation results show that, compared with extended Kalman filter (EKF), the proposed extended minimum variance filter is feasible and effective in WSNs

Adaptive Distributed Estimation over Wireless Sensor Networks with Packet Losses

Abstract — A distributed adaptive algorithm to estimate a time-varying signal, measured by a wireless sensor network, is designed and analyzed. The presence of measurement noises and of packet losses is considered. Each node of the network locally computes adaptive weights that guarantee to minimize the estimation error variance. Decentralized conditions on the weights, which ensure the stability of the estimates throughout the overall network, are also considered. A theoretical performance analysis of the scheme is carried out both in the presence of perfect and lossy links. Numerical simulations illustrate performance for various network topologies and packet loss probabilities. Index Terms — Distributed filtering; Wireless sensor networks; Networked Embedded System

Adaptive distributed estimation over wireless sensor networks with packet losses

A distributed adaptive algorithm to estimate a time-varying signal, measured by a wireless sensor network, is designed and analyzed. The presence of measurement noises and of packet losses is considered. Each node of the network locally computes adaptive weights that guarantee to minimize the estimation error variance. Decentralized conditions on the weights, which ensure the stability of the estimates throughout the overall network, are also considered. A theoretical performance analysis of the scheme is carried out both in the presence of perfect and lossy links. Numerical simulations illustrate performance for various network topologies and packet loss probabilities. © 2007 IEEE.QC 2011011

Adaptive Distributed Estimation over Wireless Sensor Networks with Packet Losses

Abstract — A distributed adaptive algorithm to estimate a time-varying signal, measured by a wireless sensor network, is designed and analyzed. The presence of measurement noises and of packet losses is considered. Each node of the network locally computes adaptive weights that guarantee to minimize the estimation error variance. Decentralized conditions on the weights, which ensure the stability of the estimates throughout the overall network, are also considered. A theoretical performance analysis of the scheme is carried out both in the presence of perfect and lossy links. Numerical simulations illustrate performance for various network topologies and packet loss probabilities. Index Terms — Distributed filtering; Wireless sensor networks; Networked Embedded System