Brief Paper - Distributed consensus filtering for jump Markov linear systems

This article studies the problem of distributed filtering for jump Markov linear systems in a not fully connected sensor network. A distributed consensus filter is developed by applying an improved interacting multiple model approach in which the mode-conditioned estimates are derived by the Kalman consensus filter and the mode probabilities are obtained in the sense of linear minimum variance. A numerical example is provided to demonstrate the effectiveness of the proposed algorithm for tracking a manoeuvring target in a sensor work with eight nodes.

[1]  Milos S. Stankovic,et al.  Consensus Based Overlapping Decentralized Estimator , 2009, IEEE Transactions on Automatic Control.

[2]  V. Jilkov,et al.  Survey of maneuvering target tracking. Part V. Multiple-model methods , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[3]  Yingmin Jia,et al.  Consensus-Based Distributed Multiple Model UKF for Jump Markov Nonlinear Systems , 2012, IEEE Transactions on Automatic Control.

[4]  Y. Bar-Shalom,et al.  The interacting multiple model algorithm for systems with Markovian switching coefficients , 1988 .

[5]  J. Lam,et al.  Brief paper - Consensus of multi-agent systems: a simultaneous stabilisation approach , 2012 .

[6]  Dongbing Gu,et al.  Cooperative Target Tracking Control of Multiple Robots , 2012, IEEE Transactions on Industrial Electronics.

[7]  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.

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

[9]  Yugang Niu,et al.  Distributed estimation and filtering for sensor networks , 2011 .

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

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

[12]  Shu-Li Sun.  Multi-sensor information fusion white noise filter weighted by scalars based on Kalman predictor , 2004, Autom..

[13]  Jian Wang,et al.  Coding scheme based on boundary function for consensus control of multi-agent system with time-varying topology , 2012 .

[14]  Y. Jia,et al.  Brief Paper - L 2 -L ∞ consensus control for high-order multi-agent systems with switching topologies and time-varying delays , 2012 .

[15]  Jorge Cortés,et al.  Distributed Kriged Kalman Filter for Spatial Estimation , 2009, IEEE Transactions on Automatic Control.

[16]  Junping Du,et al.  New interacting multiple model algorithms for the tracking of the manoeuvring target [Brief Paper] , 2010 .

[17]  Yingmin Jia,et al.  Consensus-based distributed information filter for a class of jump Markov systems , 2011 .

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

[19]  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.

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

[21]  Guanghui Wen,et al.  Stochastic consensus in directed networks of agents with non-linear dynamics and repairable actuator failures , 2012 .

[22]  Marcello Farina,et al.  Distributed Moving Horizon Estimation for Linear Constrained Systems , 2010, IEEE Transactions on Automatic Control.