A distributed maximum correntropy Kalman filter

Abstract Most distributed Kalman filters are based on the cost function of the well-known minimum mean square estimation criterion, which performs well in the presence of Gaussian noise. When impulsive noise is involved, the performance of distributed Kalman filters may become worse. Recently, a Kalman filter based on the maximum correntropy criterion has been shown to outperform the conventional Kalman filter in the case of impulsive noise. In this paper, we extend the maximum correntropy Kalman filter to a distributed algorithm by introducing a new gain matrix, and analyze the corresponding mean error and mean square error behavior. Simulations are used to demonstrate the effectiveness of the proposed algorithm.

[1]  Xiyuan Chen,et al.  Improved Cubature Kalman Filter for GNSS/INS Based on Transformation of Posterior Sigma-Points Error , 2017, IEEE Transactions on Signal Processing.

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

[3]  Danilo P. Mandic,et al.  Distributed Adaptive Filtering of $\alpha$ -Stable Signals , 2018, IEEE Signal Processing Letters.

[4]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[5]  Stefan Werner,et al.  Kalman Filtering : Consensus , Diffusion , and Mixed * , 2018 .

[6]  Liam Paull,et al.  Neural network-based multiple robot Simultaneous Localization and Mapping , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Danilo P. Mandic,et al.  Kalman filtering for widely linear complex and quaternion valued bearings only tracking , 2012, IET Signal Process..

[8]  Yeng Chai Soh,et al.  Adaptive Kalman Filtering in Networked Systems With Random Sensor Delays, Multiple Packet Dropouts and Missing Measurements , 2010, IEEE Transactions on Signal Processing.

[9]  José M. F. Moura,et al.  Distributed Kalman Filtering With Dynamic Observations Consensus , 2015, IEEE Transactions on Signal Processing.

[10]  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.

[11]  Baltasar Beferull-Lozano,et al.  Adaptive Consensus-Based Distributed Kalman Filter for WSNs with Random Link Failures , 2016, 2016 International Conference on Distributed Computing in Sensor Systems (DCOSS).

[12]  Weifeng Liu,et al.  Correntropy: Properties and Applications in Non-Gaussian Signal Processing , 2007, IEEE Transactions on Signal Processing.

[13]  Gang Wang,et al.  Complex-Valued adaptive networks based on entropy estimation , 2018, Signal Process..

[14]  T. Kailath,et al.  An innovations approach to least-squares estimation--Part II: Linear smoothing in additive white noise , 1968 .

[15]  Stefan Werner,et al.  Distributed Kalman Filtering in Presence of Unknown Outer Network Actuations , 2019, IEEE Control Systems Letters.

[16]  José M. F. Moura,et al.  Consensus+Innovations Distributed Kalman Filter With Optimized Gains , 2016, IEEE Transactions on Signal Processing.

[17]  Konstantinos N. Plataniotis,et al.  Structure-Induced Complex Kalman Filter for Decentralized Sequential Bayesian Estimation , 2015, IEEE Signal Processing Letters.

[18]  T. Kailath,et al.  An innovations approach to least-squares estimation--Part III: Nonlinear estimation in white Gaussian noise , 1971 .

[19]  Ji Zhao,et al.  Switching criterion for sub-and super-Gaussian additive noise in adaptive filtering , 2018, Signal Process..

[20]  Marcello Farina,et al.  An Approach to Distributed Predictive Control for Tracking–Theory and Applications , 2014, IEEE Transactions on Control Systems Technology.

[21]  Danilo P. Mandic,et al.  A Distributed Quaternion Kalman Filter With Applications to Smart Grid and Target Tracking , 2016, IEEE Transactions on Signal and Information Processing over Networks.

[22]  Aboelmagd Noureldin,et al.  Sensor Integration for Satellite-Based Vehicular Navigation Using Neural Networks , 2007, IEEE Transactions on Neural Networks.

[23]  Xi Liu,et al.  > Replace This Line with Your Paper Identification Number (double-click Here to Edit) < , 2022 .

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

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