Distributed Information Consensus Filters for Simultaneous Input and State Estimation

This paper describes the distributed information filtering where a set of sensor networks are required to simultaneously estimate input and state of a linear discrete-time system from collaborative manner. Our research purpose is to develop a consensus strategy in which sensor nodes communicate within the network through a sequence of Kalman iterations and data diffusion. A novel recursive information filtering is proposed by integrating input estimation error into measurement data and weighted information matrices. On the fusing process, local system state filtering transmits estimation information using the consensus averaging algorithm, which penalizes the disagreement in a dynamic manner. A simulation example is provided to compare the performance of the distributed information filtering with optimal Gillijins–De Moor’s algorithm.

[1]  Paul M. Frank,et al.  Fault Diagnosis in Dynamic Systems , 1993, Robotics, Mechatronics and Manufacturing Systems.

[2]  Milos S. Stankovic,et al.  Consensus Based Overlapping Decentralized Estimation With Missing Observations and Communication Faults , 2008 .

[3]  R. Olfati-Saber,et al.  Distributed Kalman Filter with Embedded Consensus Filters , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[4]  Nicolae Vizireanu,et al.  Quantisation-based video watermarking in the wavelet domain with spatial and temporal redundancy , 2011 .

[5]  Stephen P. Boyd,et al.  Fast linear iterations for distributed averaging , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[6]  Maithili Sharan,et al.  Weather and Climate: The M.P. Singh Volume Part II , 2005 .

[7]  Mohamed Darouach,et al.  Extension of minimum variance estimation for systems with unknown inputs , 2003, Autom..

[8]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems: theory and application , 1989 .

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

[10]  Chien-Shu Hsieh,et al.  Robust two-stage Kalman filters for systems with unknown inputs , 2000, IEEE Trans. Autom. Control..

[11]  Siro Pillosu,et al.  Consensus based decentralized estimation for linear plants with unknown inputs , 2010, 49th IEEE Conference on Decision and Control (CDC).

[12]  Bart De Moor,et al.  Unbiased minimum-variance input and state estimation for linear discrete-time systems with direct feedthrough , 2007, Autom..

[13]  B. Anderson,et al.  Optimal Filtering , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  C. E. Koeppe,et al.  Weather and climate , 1935 .

[15]  S.S. Stankovic,et al.  Consensus Based Overlapping Decentralized Estimator , 2007, 2007 American Control Conference.

[16]  Nicolae Vizireanu,et al.  Robust wavelet-based video watermarking scheme for copyright protection using the human visual system , 2011, J. Electronic Imaging.

[17]  Peter K. Kitanidis,et al.  Unbiased minimum-variance linear state estimation , 1987, Autom..

[18]  Huazhen Fang,et al.  Kalman filter-based identification for systems with randomly missing measurements in a network environment , 2010, Int. J. Control.

[19]  D.N. Vizireanu,et al.  Blind Watermarking Capacity Analysis of MPEG2 Coded Video , 2007, 2007 8th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services.

[20]  Wei Liu,et al.  Estimation of the Non-Measurable State Variables of a Transcutaneous Energy Transmission System for Artificial Human Implants Using Extended Kalman Filters , 2009, Circuits Syst. Signal Process..

[21]  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.

[22]  Huazhen Fang,et al.  Genetic adaptive state estimation with missing input/output data , 2010 .

[23]  X. Rong Li,et al.  A Survey of Maneuvering Target Tracking—Part IV: Decision-Based Methods , 2002 .

[24]  Dan Simon,et al.  Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches , 2006 .

[25]  Bart De Moor,et al.  Unbiased minimum-variance input and state estimation for linear discrete-time systems , 2007, Autom..

[26]  Dragos Nicolae Vizireanu,et al.  A robust digital watermarking scheme for video copyright protection in the wavelet domain , 2010 .

[27]  El Kebir Boukas,et al.  Kalman Filtering for Affine Nonlinear Descriptor Systems , 2011, Circuits Syst. Signal Process..

[28]  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.

[29]  José M. F. Moura,et al.  Distributing the Kalman Filter for Large-Scale Systems , 2007, IEEE Transactions on Signal Processing.