Networked fusion kalman filtering with multiple uncertainties

This paper investigates the problem of fusion filtering for a class of networked multisensor fusion systems with multiple uncertainties, including sensor failures, stochastic parameter uncertainties, random observation delays, and packet dropouts. A novel model is proposed to describe the random observation delays and packet dropouts, and a robust optimal fusion filter for the addressed networked multisensor fusion systems is designed using the innovation analysis method. The dimension of the designed filter is the same as that of the original system, which helps to reduce computation cost compared with the augmentation method. Moreover, robust reduced-dimension observation-fusion Kalman filters are proposed to further reduce the computation burden. Note that the designed fusion filter gain matrices can be computed off-line, as they depend only on the upper bounds of random delays and on the occurrence probabilities of delays and sensor failures. Some sufficient conditions are presented for stability and optimality of the designed fusion filters, and a steady-state fusion filter is also given for the networked multisensor fusion systems. Simulations show the effectiveness of the proposed fusion filters.

[1]  Wen-an Zhang,et al.  Distributed Finite-Horizon Fusion Kalman Filtering for Bandwidth and Energy Constrained Wireless Sensor Networks , 2014, IEEE Transactions on Signal Processing.

[2]  Jing Ma,et al.  Information fusion estimators for systems with multiple sensors of different packet dropout rates , 2011, Inf. Fusion.

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

[4]  Sirish L. Shah,et al.  Optimal H2 filtering with random sensor delay, multiple packet dropout and uncertain observations , 2007, Int. J. Control.

[5]  Ming Zeng,et al.  Distributed weighted robust Kalman filter fusion for uncertain systems with autocorrelated and cross-correlated noises , 2013, Inf. Fusion.

[6]  Fuwen Yang,et al.  Decentralized robust Kalman filtering for uncertain stochastic systems over heterogeneous sensor networks , 2008, Signal Process..

[7]  Okuary Osechas,et al.  Distributed ionosphere monitoring by collaborating mobile receivers , 2014, IEEE Transactions on Aerospace and Electronic Systems.

[8]  Guoqiang Hu,et al.  Distributed Fusion Estimation With Communication Bandwidth Constraints , 2015, IEEE Transactions on Automatic Control.

[9]  Gang Feng,et al.  Multi-rate distributed fusion estimation for sensor networks with packet losses , 2012, Autom..

[10]  Balasubramaniam Natarajan,et al.  State Estimation Over a Lossy Network in Spatially Distributed Cyber-Physical Systems , 2014, IEEE Transactions on Signal Processing.

[11]  C. J. Harris,et al.  Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion , 2001 .

[12]  Alessandro Chiuso,et al.  Information fusion strategies and performance bounds in packet-drop networks , 2011, Autom..

[13]  Yuanqing Xia,et al.  Networked Filtering and Fusion in Wireless Sensor Networks , 2014 .

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

[15]  Zhansheng Duan,et al.  Lossless Linear Transformation of Sensor Data for Distributed Estimation Fusion , 2011, IEEE Transactions on Signal Processing.

[16]  Edwin Engin Yaz,et al.  Robust minimum variance linear state estimators for multiple sensors with different failure rates , 2007, Autom..

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

[18]  Fuwen Yang,et al.  Robust finite-horizon filtering for stochastic systems with missing measurements , 2005, IEEE Signal Processing Letters.

[19]  Yuanqing Xia,et al.  Analysis and Synthesis of Networked Control Systems , 2011 .

[20]  Pramod K. Varshney,et al.  Recursive estimation with uncertain observations in a multisensor environment , 1986 .

[21]  Roberto Sanchis,et al.  Estimation in multisensor networked systems with scarce measurements and time varying delays , 2012, Syst. Control. Lett..

[22]  Wen-an Zhang,et al.  Robust Information Fusion Estimator for Multiple Delay-Tolerant Sensors With Different Failure Rates , 2013, IEEE Transactions on Circuits and Systems I: Regular Papers.

[23]  Fan Wang,et al.  Robust Kalman filters for linear time-varying systems with stochastic parametric uncertainties , 2002, IEEE Trans. Signal Process..

[24]  Donghua Zhou,et al.  Robust Fault Detection for Networked Systems with Distributed Sensors , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[25]  Jing Ma,et al.  Optimal Linear Estimators for Systems With Random Sensor Delays, Multiple Packet Dropouts and Uncertain Observations , 2011, IEEE Transactions on Signal Processing.

[26]  Shuli Sun,et al.  Centralized Fusion Estimators for Multisensor Systems With Random Sensor Delays, Multiple Packet Dropouts and Uncertain Observations , 2013, IEEE Sensors Journal.

[27]  Yunmin Zhu,et al.  Optimal Kalman filtering fusion with cross-correlated sensor noises , 2007, Autom..

[28]  A. Jazwinski.  Stochastic Processes and Filtering Theory , 1970 .

[29]  Yuan Gao,et al.  Self-Tuning Multisensor Weighted Measurement Fusion Kalman Filter , 2009, IEEE Transactions on Aerospace and Electronic Systems.

[30]  Venkatesh Saligrama,et al.  Distributed Tracking in Multihop Sensor Networks With Communication Delays , 2007, IEEE Transactions on Signal Processing.

[31]  Yuanqing Xia,et al.  Networked Data Fusion With Packet Losses and Variable Delays , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[32]  Huijun Gao,et al.  Stabilization of Networked Control Systems With a New Delay Characterization , 2008, IEEE Transactions on Automatic Control.

[33]  P. L. Odell,et al.  Full Rank Factorization of Matrices , 1999 .

[34]  Jean-Yves Tourneret,et al.  Least-squares estimation of multiple abrupt changes contaminated by multiplicative noise using MCMC , 1999, Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics. SPW-HOS '99.

[35]  Luca Schenato,et al.  Optimal sensor fusion for distributed sensors subject to random delay and packet loss , 2007, 2007 46th IEEE Conference on Decision and Control.

[36]  Stephen P. Boyd,et al.  Linear Matrix Inequalities in Systems and Control Theory , 1994 .

[37]  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.