Robust Adaptive Central Difference Particle Filter

This paper presents a new robust adaptive central difference particle filtering method for nonlinear systems by combining the concept of robust adaptive estimation with the central difference particle filter. This method obtains system state estimate and covariances using the principle of robust estimation. Subsequently, the importance density is obtained by adjusting the state estimate and covariances through the equivalent weight function and adaptive factor constructed from predicted residuals to control the contributions to the new state estimation from measurement and kinematic model. The proposed method can not only minimize the variance of the importance density distribution to resist the disturbances of systematic noises, but it also fully takes advantage of present measurement information to avoid particle degeneration. Experiments and comparison analysis with the existing methods demonstrate the improved filtering accuracy of the proposed method.

[1]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[2]  Rudolph van der Merwe,et al.  Gaussian mixture sigma-point particle filters for sequential probabilistic inference in dynamic state-space models , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[3]  J.V. Candy,et al.  Bootstrap Particle Filtering , 2007, IEEE Signal Processing Magazine.

[4]  Moon Gi Kang,et al.  Super-resolution image reconstruction , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).

[5]  Martin M. Andreasen.  Non-linear DSGE Models and The Optimized Central Difference Particle Filter , 2010 .

[6]  Niels Kjølstad Poulsen,et al.  New developments in state estimation for nonlinear systems , 2000, Autom..

[7]  P. Moral,et al.  Sequential Monte Carlo samplers , 2002, cond-mat/0212648.

[8]  Dieter Fox,et al.  Real-time particle filters , 2004, Proceedings of the IEEE.

[9]  Gianluigi Pillonetto,et al.  Optimal smoothing of non-linear dynamic systems via Monte Carlo Markov chains , 2008, Autom..

[10]  P. Djurić,et al.  Particle filtering , 2003, IEEE Signal Process. Mag..

[11]  Rudolph van der Merwe,et al.  Sigma-point kalman filters for probabilistic inference in dynamic state-space models , 2004 .

[12]  Wei Li,et al.  Robust adaptive filtering method for SINS/SAR integrated navigation system , 2011 .

[13]  Christian Musso,et al.  Improving Regularised Particle Filters , 2001, Sequential Monte Carlo Methods in Practice.

[14]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

[15]  Georges Oppenheim,et al.  The particle filters and their applications , 2008 .

[16]  Pingyuan Cui,et al.  Adaptive MCMC Particle Filter for Nonlinear and Non-Gaussian State Estimation , 2008, 2008 3rd International Conference on Innovative Computing Information and Control.

[17]  Jinling Wang,et al.  Adaptive estimation of multiple fading factors in Kalman filter for navigation applications , 2008 .

[18]  Petar M. Djuric,et al.  Gaussian particle filtering , 2003, IEEE Trans. Signal Process..

[19]  Xianqiang Cui,et al.  Adaptively robust filter with multi adaptive factors , 2008 .

[20]  Simon J. Godsill,et al.  An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo , 2007, Proceedings of the IEEE.

[21]  A. Doucet,et al.  A note on auxiliary particle filters , 2008 .

[22]  Youan Zhang,et al.  Central Difference Particle Filter Applied to Transfer Alignment for SINS on Missiles , 2012, IEEE Transactions on Aerospace and Electronic Systems.

[23]  Frank Dellaert,et al.  MCMC-based particle filtering for tracking a variable number of interacting targets , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[25]  Joseph J. LaViola,et al.  On Kalman Filtering With Nonlinear Equality Constraints , 2007, IEEE Transactions on Signal Processing.

[26]  Yuanxi Yang,et al.  Adaptively robust filtering for kinematic geodetic positioning , 2001 .

[27]  Jean-Yves Tourneret,et al.  A Particle Filtering Approach for Joint Detection/Estimation of Multipath Effects on GPS Measurements , 2007, IEEE Transactions on Signal Processing.

[28]  Simo Särkkä,et al.  On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems , 2007, IEEE Trans. Autom. Control..

[29]  Herman Bruyninckx,et al.  Kalman filters for non-linear systems: a comparison of performance , 2004 .

[30]  Kazufumi Ito,et al.  Gaussian filters for nonlinear filtering problems , 2000, IEEE Trans. Autom. Control..