Distributed weighted fusion estimation for uncertain networked systems with transmission time-delay and cross-correlated noises

Abstract This paper investigates the state estimation issue for uncertain networked systems considering data transmission time-delay and cross-correlated noises. A distributed robust Kalman filtering-based perception and centralized fusion method is proposed to improve the estimation accuracy from perturbed measurement; consequently, reduce the amount of redundant information and alleviate the estimation burden. To describe the transmission time-delay and give rise to cross-correlated and state-dependent noises in the exchange measurement among neighbors, a weighted fusion reorganized innovation strategy is proposed to reduce the computational burden and suppress noise effect. Moreover, to obtain the optimal linear estimate, a fusion estimation approach is used for information collaboration by weighting the error cross-covariance matrices. Finally, an illustrative example is presented to demonstrate the effectiveness and robustness of the proposed method.

[1]  Faridoon Shabaninia,et al.  Fuzzy Kalman-type filter for interval fractional-order systems with finite-step auto-correlated process noises , 2015, Neurocomputing.

[2]  Oswaldo Luiz V. Costa,et al.  Robust mode-independent filtering for discrete-time Markov jump linear systems with multiplicative noises , 2013, Int. J. Control.

[3]  A. Benjamin Premkumar,et al.  A distributed particle filtering approach for multiple acoustic source tracking using an acoustic vector sensor network , 2015, Signal Process..

[4]  Ming Zeng,et al.  Optimal distributed Kalman filtering fusion for a linear dynamic system with cross-correlated noises , 2012, Int. J. Syst. Sci..

[5]  Wen-An Zhang,et al.  Distributed consensus-based Kalman filtering in sensor networks with quantised communications and random sensor failures , 2014, IET Signal Process..

[6]  Feng Ding,et al.  Several multi-innovation identification methods , 2010, Digit. Signal Process..

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

[8]  Qing-Long Han,et al.  Distributed networked control systems: A brief overview , 2017, Inf. Sci..

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

[10]  Jie Xu,et al.  Optimal Distributed Kalman Filtering Fusion With Singular Covariances of Filtering Errors and Measurement Noises , 2014, IEEE Transactions on Automatic Control.

[11]  Huijun Gao,et al.  A Parameter-Dependent Approach to Robust $H_{\infty }$ Filtering for Time-Delay Systems , 2008, IEEE Transactions on Automatic Control.

[12]  Peng Zhang,et al.  Robust weighted fusion Kalman filters for multisensor time-varying systems with uncertain noise variances , 2014, Signal Process..

[13]  Reza Mahboobi Esfanjani,et al.  Improved robust finite-horizon Kalman filtering for uncertain networked time-varying systems , 2015, Inf. Sci..

[14]  Yuan Gao,et al.  New approach to information fusion steady-state Kalman filtering , 2005, Autom..

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

[16]  H. Vincent Poor,et al.  Distributed Kalman Filtering Over Massive Data Sets: Analysis Through Large Deviations of Random Riccati Equations , 2014, IEEE Transactions on Information Theory.

[17]  Yeung Sam Hung,et al.  Distributed H∞-consensus filtering in sensor networks with multiple missing measurements: The finite-horizon case , 2010, Autom..

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

[19]  Peng Zhang,et al.  Robust Sequential Covariance Intersection Fusion Kalman Filtering over Multi-agent Sensor Networks with Measurement Delays and Uncertain Noise Variances , 2014 .

[20]  Isaac Yaesh,et al.  Hinfinity control and filtering of discrete-time stochastic systems with multiplicative noise , 2001, Autom..

[21]  Wen-an Zhang,et al.  Distributed Fusion Estimation With Missing Measurements, Random Transmission Delays and Packet Dropouts , 2014, IEEE Transactions on Automatic Control.

[22]  Jun Hu,et al.  Estimation, filtering and fusion for networked systems with network-induced phenomena: New progress and prospects , 2016, Inf. Fusion.

[23]  Huajing Fang,et al.  Recursive estimation for nonlinear stochastic systems with multi-step transmission delays, multiple packet dropouts and correlated noises , 2015, Signal Process..

[24]  Xiao Lu,et al.  Kalman filtering for multiple time-delay systems , 2005, Autom..

[25]  Li Sheng,et al.  Some remarks on stability of stochastic singular systems with state-dependent noise , 2015, Autom..

[26]  Gang Feng,et al.  Linear estimation for random delay systems , 2010, 49th IEEE Conference on Decision and Control (CDC).

[27]  David Zhang,et al.  An innovation approach to Hinfinity prediction for continuous-time systems with application to systems with delayed measurements , 2004, Autom..

[28]  Stephen P. Boyd,et al.  Fast linear iterations for distributed averaging , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[29]  Shu-Li Sun,et al.  Multi-sensor optimal information fusion Kalman filter , 2004, Autom..

[30]  Bo Song,et al.  filtering for stochastic systems driven by Poisson processes , 2015, Int. J. Control.

[31]  Peng Shi,et al.  Joint state filtering and parameter estimation for linear stochastic time-delay systems , 2011, Signal Process..

[32]  Peng Zhang,et al.  Hierarchical fusion robust Kalman filter for clustering sensor network time‐varying systems with uncertain noise variances , 2015 .

[33]  Bor-Sen Chen,et al.  Robust sensorimotor control of human arm model under state-dependent noises, control-dependent noises and additive noises , 2015, Neurocomputing.

[34]  Marios M. Polycarpou,et al.  Distributed Sensor Fault Diagnosis for a Network of Interconnected Cyberphysical Systems , 2015, IEEE Transactions on Control of Network Systems.

[35]  Tao Jiang,et al.  Energy Cost Minimization for Distributed Internet Data Centers in Smart Microgrids Considering Power Outages , 2015, IEEE Transactions on Parallel and Distributed Systems.

[36]  Yuanqing Xia,et al.  Optimal sequential and distributed fusion for state estimation in cross-correlated noise , 2013, Autom..

[37]  Shouming Zhong,et al.  State estimation for neural networks with multiple time delays , 2015, Neurocomputing.

[38]  Qing-Long Han,et al.  Distributed event-triggered H1 filtering over sensor networks with communication delays , 2014 .

[39]  Aurora Hermoso-Carazo,et al.  Distributed fusion estimation in networked systems with uncertain observations and Markovian random delays , 2015, Signal Process..

[40]  Na Li,et al.  Multi-sensor information fusion estimators for stochastic uncertain systems with correlated noises , 2016, Inf. Fusion.

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

[42]  Fei Meng,et al.  An extended Kalman filter for input estimations in diesel-engine selective catalytic reduction applications , 2016, Neurocomputing.

[43]  Yong-An Zhang,et al.  Some results on linear equality constrained state filtering , 2013, Int. J. Control.