Data-driven communication for state estimation with sensor networks

This paper deals with the problem of estimating the state of a discrete-time linear stochastic dynamical system on the basis of data collected from multiple sensors subject to a limitation on the communication rate from the sensors. More specifically, the attention is devoted to a centralized sensor network consisting of: (1) multiple remote nodes which collect measurements of the given system, compute state estimates at the full measurement rate and transmit data (either raw measurements or estimates) at a reduced communication rate; (2) a fusion node that, based on received data, provides an estimate of the system state at the full rate. Local data-driven transmission strategies are considered and issues related to the stability and performance of such strategies are investigated. Simulation results confirm the effectiveness of the proposed strategies.

[1]  Richard M. Murray,et al.  DISTRIBUTED SENSOR FUSION USING DYNAMIC CONSENSUS , 2005 .

[2]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[3]  Nandit Soparkar,et al.  Trading computation for bandwidth: reducing communication in distributed control systems using state estimators , 2002, IEEE Trans. Control. Syst. Technol..

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

[5]  Young Soo Suh,et al.  Modified Kalman filter for networked monitoring systems employing a send-on-delta method , 2007, Autom..

[6]  Jeffrey K. Uhlmann,et al.  A non-divergent estimation algorithm in the presence of unknown correlations , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[7]  Panos J. Antsaklis,et al.  Linear Systems , 1997 .

[8]  Subhrakanti Dey,et al.  Stability of Kalman filtering with Markovian packet losses , 2007, Autom..

[9]  K. Åström,et al.  Comparison of Riemann and Lebesgue sampling for first order stochastic systems , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[10]  Richard M. Murray,et al.  On a stochastic sensor selection algorithm with applications in sensor scheduling and sensor coverage , 2006, Autom..

[11]  Giorgio Battistelli,et al.  State estimation in a centralized sensor network under limited communication rate , 2009, 2009 European Control Conference (ECC).

[12]  Chongzhao Han,et al.  Optimal linear estimation fusion .I. Unified fusion rules , 2003, IEEE Trans. Inf. Theory.

[13]  Richard M. Murray,et al.  Optimal LQG control across packet-dropping links , 2007, Syst. Control. Lett..

[14]  Martin Nilsson,et al.  Investigating the energy consumption of a wireless network interface in an ad hoc networking environment , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[15]  Ruggero Carli,et al.  Distributed Kalman filtering based on consensus strategies , 2008, IEEE Journal on Selected Areas in Communications.

[16]  W. Wong,et al.  State estimation with communication constraints , 1996 .

[17]  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.

[18]  Giorgio Battistelli,et al.  State estimation with remote sensors and intermittent transmissions , 2012, Syst. Control. Lett..

[19]  J.P. Hespanha,et al.  Estimation under uncontrolled and controlled communications in Networked Control Systems , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[20]  R. Brockett,et al.  Systems with finite communication bandwidth constraints. I. State estimation problems , 1997, IEEE Trans. Autom. Control..

[21]  B. Anderson,et al.  Detectability and Stabilizability of Time-Varying Discrete-Time Linear Systems , 1981 .

[22]  G. Battistelli,et al.  State estimation with a remote sensor under limited communication rate , 2008, 2008 3rd International Symposium on Communications, Control and Signal Processing.

[23]  Meir Feder,et al.  Rate-distortion performance in coding bandlimited sources by sampling and dithered quantization , 1995, IEEE Trans. Inf. Theory.

[24]  Orest Iftime,et al.  Proceedings of the 16th IFAC World congress , 2006 .