Consensus variable structure multiple model filtering for distributed maneuvering tracking

Abstract This paper considers the problem of maneuvering target tracking for distributed sensor networks. Consensus-based multiple model (MM) filters are effective methods to deal with this problem. However, the model set of these filters is fixed. When the scene is complicated and the model set for MM methods is large, these methods may become inefficient and performance may decrease while the variable structure multiple model (VSMM) method can handle it well. The VSMM version of consensus filter based on the expected mode augmentation (EMA) is presented in this paper to track maneuvering target for distributed sensor networks. The distributed consensus filter for VSMM is presented first and it runs parallel consensus on both the mode probability and the mode-matched probability density function (PDF). Then the EMA algorithm is used to determine the model set utilized at each time step. Simulation results demonstrate the superiority of the proposed method.

[1]  X. R. Li,et al.  Multiple-model estimation with variable structure. IV. Design and evaluation of model-group switching algorithm , 1999 .

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

[3]  Reza Olfati-Saber,et al.  Consensus and Cooperation in Networked Multi-Agent Systems , 2007, Proceedings of the IEEE.

[4]  Zhongliang Jing,et al.  A distributed consensus filter for sensor networks with heavy-tailed measurement noise , 2018, Science China Information Sciences.

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

[6]  Frank L. Lewis,et al.  Distributed information-weighted Kalman consensus filter for sensor networks , 2017, Autom..

[7]  X. Rong Li,et al.  Multiple-model estimation with variable structure. II. Model-set adaptation , 2000, IEEE Trans. Autom. Control..

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

[9]  Henry Leung,et al.  Robust Consensus Nonlinear Information Filter for Distributed Sensor Networks With Measurement Outliers , 2019, IEEE Transactions on Cybernetics.

[10]  Konstantinos N. Plataniotis,et al.  Distributed Widely Linear Multiple-Model Adaptive Estimation , 2015, IEEE Transactions on Signal and Information Processing over Networks.

[11]  X. Rong Li,et al.  Multiple-model estimation with variable structure- part VI: expected-mode augmentation , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[12]  G. Battistelli,et al.  An Information-Theoretic Approach to Distributed State Estimation , 2011 .

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

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

[15]  Youmin Zhang,et al.  Multiple-model estimation with variable structure. V. Likely-model set algorithm , 2000, IEEE Trans. Aerosp. Electron. Syst..

[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]  Giorgio Battistelli,et al.  Consensus-Based Linear and Nonlinear Filtering , 2015, IEEE Transactions on Automatic Control.

[18]  Junping Du,et al.  Distributed extended Kalman filter with nonlinear consensus estimate , 2017, J. Frankl. Inst..

[19]  X. R. Li,et al.  Multiple-model estimation with variable structure. III. Model-group switching algorithm , 1999 .

[20]  Zhongliang Jing,et al.  A Consensus Nonlinear Filter With Measurement Uncertainty in Distributed Sensor Networks , 2017, IEEE Signal Processing Letters.

[21]  Hamid Khaloozadeh,et al.  Consensus-based distributed unscented target tracking in wireless sensor networks with state-dependent noise , 2018, Signal Process..

[22]  Giorgio Battistelli,et al.  Stability of consensus extended Kalman filter for distributed state estimation , 2016, Autom..

[23]  Giorgio Battistelli,et al.  Kullback-Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stability , 2014, Autom..

[24]  Junping Du,et al.  Brief Paper - Distributed consensus filtering for jump Markov linear systems , 2013 .

[25]  S. Haykin,et al.  Cubature Kalman Filters , 2009, IEEE Transactions on Automatic Control.

[26]  Y. Bar-Shalom,et al.  Multiple-model estimation with variable structure , 1996, IEEE Trans. Autom. Control..

[27]  Giorgio Battistelli,et al.  Consensus-based multiple-model Bayesian filtering for distributed tracking , 2015 .

[28]  Zhongliang Jing,et al.  Simultaneous target tracking and sensor location refinement in distributed sensor networks , 2018, Signal Process..