Weighted Measurement Fusion Particle Filter for Nonlinear Systems with Correlated Noises

The multi-sensor information fusion particle filter (PF) has been put forward for nonlinear systems with correlated noises. The proposed algorithm uses the Taylor series expansion method, which makes the nonlinear measurement functions have a linear relationship by the intermediary function. A weighted measurement fusion PF (WMF-PF) was put forward for systems with correlated noises by applying the full rank decomposition and the weighted least square theory. Compared with the augmented optimal centralized fusion particle filter (CF-PF), it could greatly reduce the amount of calculation. Moreover, it showed asymptotic optimality as the Taylor series expansion increased. The simulation examples illustrate the effectiveness and correctness of the proposed algorithm.

[1]  J. Hammersley,et al.  Poor Man's Monte Carlo , 1954 .

[2]  G. Kitagawa.  Non-Gaussian state space modeling of time series , 1987, 26th IEEE Conference on Decision and Control.

[3]  Sun Shu-li.  Weighted measurement fusion estimation algorithm with correlated noises and its global optimality , 2010 .

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

[5]  Yunmin Zhu,et al.  Sensors' optimal dimensionality compression matrix in estimation fusion , 2005, Autom..

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

[7]  Yuanwei Jing,et al.  UKF design and stability for nonlinear stochastic systems with correlated noises , 2007, 2007 46th IEEE Conference on Decision and Control.

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

[9]  Arnaud Doucet,et al.  A survey of convergence results on particle filtering methods for practitioners , 2002, IEEE Trans. Signal Process..

[10]  Marcelo G. S. Bruno,et al.  Improved particle filters for ballistic target tracking , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[11]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[12]  Han Chongzhao.  Multi-sensor centralized fusion tracking with correlated measurement noises , 2005 .

[13]  Xiaoxu Wang,et al.  Gaussian filter for nonlinear systems with correlated noises at the same epoch , 2015, Autom..

[14]  Quan Pan,et al.  Design and implementation of Gaussian filter for nonlinear system with randomly delayed measurements and correlated noises , 2014, Appl. Math. Comput..

[15]  Zhang Hong-yue.  Multiple Correlated Measurements Fusion Algorithm and Its Optimality , 2005 .

[16]  Chenglin Wen,et al.  Multisensor Nonlinear Fusion Methods Based on Adaptive Ensemble Fifth-Degree Iterated Cubature Information Filter for Biomechatronics , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[17]  Simon Haykin,et al.  Cubature Kalman Filtering for Continuous-Discrete Systems: Theory and Simulations , 2010, IEEE Transactions on Signal Processing.

[18]  Domenico Guida,et al.  System identification and experimental modal analysis of a frame structure , 2018 .

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

[20]  Domenico Guida,et al.  System Identification Algorithm for Computing the Modal Parameters of Linear Mechanical Systems , 2018 .

[21]  Chi K. Tse,et al.  Novel cubature Kalman filtering for systems involving nonlinear states and linear measurements , 2015 .

[22]  Xin Wang,et al.  Weighted Measurement Fusion White Noise Deconvolution Filter with Correlated Noise for Multisensor Stochastic Systems , 2012 .

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

[24]  Yair Be'ery,et al.  Decentralized estimation of regression coefficients in sensor networks , 2017, Digit. Signal Process..

[25]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[26]  Ran Chen.  Correlated Measurement Fusion Steady-state Kalman Filtering Algorithms and Their Optimality , 2008 .

[27]  Quan Pan,et al.  A Gaussian approximation recursive filter for nonlinear systems with correlated noises , 2012, Autom..

[28]  Bernd A. Berg.  Markov Chain Monte Carlo Simulations and Their Statistical Analysis , 2004 .

[29]  Yakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking: Principles and Techniques , 1995 .

[30]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[31]  Yunmin Zhu,et al.  The Optimal Kalman Type State Estimator with Multi-Step Correlated Process and Measurement Noises , 2008, 2008 International Conference on Embedded Software and Systems.

[32]  Alberto Trevisani,et al.  State estimation using multibody models and non-linear Kalman filters , 2012 .

[33]  Li Liang.  Design of unscented Kalman filter with correlative noises , 2010 .

[34]  Domenico Guida,et al.  A time-domain system identification numerical procedure for obtaining linear dynamical models of multibody mechanical systems , 2018 .

[35]  Chenglin Wen,et al.  Carrier Tracking Estimation Analysis by Using the Extended Strong Tracking Filtering , 2017, IEEE Transactions on Industrial Electronics.

[36]  Jing Ma,et al.  Multi-sensor distributed fusion estimation with applications in networked systems: A review paper , 2017, Inf. Fusion.

[37]  Hong-yan Wei.  Design of UKF with correlative noises based on minimum mean square error estimation , 2010 .

[38]  Salvatore Strano,et al.  Accurate state estimation for a hydraulic actuator via a SDRE nonlinear filter , 2016 .

[39]  Salvatore Strano,et al.  An extended Kalman Filter procedure for damage detection of base-isolated structures , 2014, 2014 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems Proceedings.

[40]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[41]  Mario Innocenti,et al.  Experimental application of extended Kalman filtering for sensor validation , 2001, IEEE Trans. Control. Syst. Technol..