Online weight design for distributed filtering with limited power

In this study, the authors consider distributed estimation over a sensor network with limited power. Based on PageRank algorithm, they propose a distributed estimator, where the link weight is designed using online information. They compared the network performance of the proposed estimator with time-varying weight and constant weight under identical initial conditions. To extend the lifetime of sensors, they introduce an online power scheduling method to distributed estimation problem, where each sensor allocates the power for the communication link based on real-time estimation error. They also provide a sufficient condition to guarantee the stability of the networked system with power scheduling.

[1]  Jiming Chen,et al.  Optimal Sensor Data Scheduling for Remote Estimation Over a Time-Varying Channel , 2017, IEEE Transactions on Automatic Control.

[2]  R.W. Beard,et al.  Multi-agent Kalman consensus with relative uncertainty , 2005, Proceedings of the 2005, American Control Conference, 2005..

[3]  Chao Yang,et al.  False data injection attack on consensus‐based distributed estimation , 2017 .

[4]  Altug Iftar,et al.  Decentralized estimation and control with overlapping input, state, and output decomposition , 1990, Autom..

[5]  Wenwu Yu,et al.  Distributed Consensus Filtering in Sensor Networks , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  Chao Yang,et al.  Stochastic link activation for distributed filtering under sensor power constraint , 2017, Autom..

[7]  Milos S. Stankovic,et al.  Consensus based overlapping decentralized estimation with missing observations and communication faults , 2009, Autom..

[8]  Chao Yang,et al.  Nodes selection strategy in cooperative tracking problem , 2016, Autom..

[9]  C. Sanders,et al.  A new class of decentralized filters for interconnected systems , 1974 .

[10]  Yeung Sam Hung,et al.  Distributed H∞-consensus filtering in sensor networks with multiple missing measurements: The finite-horizon case , 2010, Autom..

[11]  Michael A. Demetriou.  Design of consensus and adaptive consensus filters for distributed parameter systems , 2010, Autom..

[12]  Ling Shi,et al.  Online sensor transmission power schedule for remote state estimation , 2013, 52nd IEEE Conference on Decision and Control.

[13]  Richard M. Murray,et al.  Approximate distributed Kalman filtering in sensor networks with quantifiable performance , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[14]  Xin Wang,et al.  Minimizing Transmit Power for Coherent Communications in Wireless Sensor Networks With Finite-Rate Feedback , 2008, IEEE Transactions on Signal Processing.

[15]  Ling Shi,et al.  Stochastic sensor activation for distributed state estimation over a sensor network , 2014, Autom..

[16]  Reza Olfati-Saber,et al.  Kalman-Consensus Filter : Optimality, stability, and performance , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[17]  Hugh F. Durrant-Whyte,et al.  A Fully Decentralized Multi-Sensor System For Tracking and Surveillance , 1993, Int. J. Robotics Res..

[18]  Ghassan Al-Regib,et al.  Rate-Constrained Distributed Estimation in Wireless Sensor Networks , 2007, IEEE Trans. Signal Process..

[19]  Reza Olfati-Saber,et al.  Distributed Kalman filtering for sensor networks , 2007, 2007 46th IEEE Conference on Decision and Control.

[20]  Zhong Liu,et al.  Adaptive fast consensus algorithm for distributed sensor fusion , 2010, Signal Process..

[21]  R. Olfati-Saber,et al.  Consensus Filters for Sensor Networks and Distributed Sensor Fusion , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[22]  Alejandro Ribeiro,et al.  Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals , 2008, IEEE Transactions on Signal Processing.

[23]  Ali H. Sayed,et al.  Diffusion Strategies for Distributed Kalman Filtering and Smoothing , 2010, IEEE Transactions on Automatic Control.