On Kalman filtering with fading wireless channels governed by power control

Abstract We study stochastic stability for Kalman filtering over fading wireless channels where variable channel gains are counteracted by the use of power control to alleviate the effects of packet drops. The Kalman filter and the controller are located at a single gateway which acquires data from the wireless sensors. We establish sufficient conditions which ensure that the Kalman filter covariance matrix is exponentially bounded in norm. The conditions obtained are then used to formulate stabilizing optimal power allocation laws which minimize the total sensor power budget. In deriving the optimal power allocation laws, both statistical channel information and full channel information are considered. The effect of system instability on the power budget is also investigated for both these cases.

[1]  Xuemin Shen,et al.  Wireless sensor networking [Guest Editorial] , 2007 .

[2]  Konrad Reif,et al.  Stochastic Stability of the Extended Kalman Filter With Intermittent Observations , 2010, IEEE Transactions on Automatic Control.

[3]  Petros G. Voulgaris,et al.  On optimal ℓ∞ to ℓ∞ filtering , 1995, Autom..

[4]  Hamid Gharavi,et al.  Special issue on sensor networks and applications , 2003 .

[5]  A. Ahlen,et al.  On the Energy-Efficiency of Cooperative MIMO in Nakagami-Fading Wireless Sensor Networks , 2007, 2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers.

[6]  Ling Shi,et al.  Kalman Filtering Over a Packet-Dropping Network: A Probabilistic Perspective , 2010, IEEE Transactions on Automatic Control.

[7]  Andrea J. Goldsmith,et al.  Linear Coherent Decentralized Estimation , 2006, IEEE Transactions on Signal Processing.

[8]  Anders Ahlén,et al.  Fixed Link Margins Outperform Power Control in Energy-Limited Wireless Sensor Networks , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[9]  Abbas Jamalipour,et al.  Wireless communications , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[10]  Luca Schenato,et al.  Optimal Estimation in Networked Control Systems Subject to Random Delay and Packet Drop , 2008, IEEE Transactions on Automatic Control.

[11]  João Pedro Hespanha,et al.  A Survey of Recent Results in Networked Control Systems , 2007, Proceedings of the IEEE.

[12]  John S. Baras,et al.  Optimal Output Feedback Control Using Two Remote Sensors Over Erasure Channels , 2009, IEEE Transactions on Automatic Control.

[13]  Iven Mareels,et al.  Optimal Power Analysis for Network Lifetime Balance in Hierarchy Networks , 2008 .

[14]  T. Katayama On the matrix Riccati equation for linear systems with random gain , 1976 .

[15]  J.E. Mazo,et al.  Digital communications , 1985, Proceedings of the IEEE.

[16]  Daniel E. Quevedo,et al.  Energy Efficient State Estimation With Wireless Sensors Through the Use of Predictive Power Control and Coding , 2010, IEEE Transactions on Signal Processing.

[17]  Robin J. Evans,et al.  Feedback Control Under Data Rate Constraints: An Overview , 2007, Proceedings of the IEEE.

[18]  M. Sternad,et al.  Unbiased power prediction of Rayleigh fading channels , 2002, Proceedings IEEE 56th Vehicular Technology Conference.

[19]  Lihua Xie,et al.  Stability of a random Riccati equation with Markovian binary switching , 2007, 2007 46th IEEE Conference on Decision and Control.

[20]  Erik Björnemo,et al.  Energy Constrained Wireless Sensor Networks : Communication Principles and Sensing Aspects , 2009 .

[21]  D. Luenberger Optimization by Vector Space Methods , 1968 .

[22]  Jamie S. Evans,et al.  Kalman filtering with faded measurements , 2009, Autom..

[23]  Ian F. Akyildiz,et al.  Wireless sensor networks , 2007 .

[24]  Bruno Sinopoli,et al.  Foundations of Control and Estimation Over Lossy Networks , 2007, Proceedings of the IEEE.

[25]  Xuemin Shen,et al.  4G mobile communications: toward open wireless architecture , 2004, IEEE Wireless Communications.

[26]  Minyue Fu,et al.  Statistical properties of the error covariance in a Kalman filter with random measurement losses , 2010, 49th IEEE Conference on Decision and Control (CDC).

[27]  Bruno Sinopoli,et al.  Kalman filtering with intermittent observations , 2004, IEEE Transactions on Automatic Control.

[28]  Richard R. Brooks Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems , 2008 .

[29]  Davide Brunelli,et al.  Wireless Sensor Networks , 2012, Lecture Notes in Computer Science.