A Systematization of the Unscented Kalman Filter Theory

In this paper, we propose a systematization of the (discrete-time) Unscented Kalman Filter (UKF) theory. We gather all available UKF variants in the literature, present corrections to theoretical inconsistencies, and provide a tool for the construction of new UKF's in a consistent way. This systematization is done, mainly, by revisiting the concepts of Sigma-Representation, Unscented Transformation (UT), Scaled Unscented Transformation (SUT), UKF, and Square-Root Unscented Kalman Filter (SRUKF). Inconsistencies are related to 1) matching the order of the transformed covariance and cross-covariance matrices of both the UT and the SUT; 2) multiple UKF definitions; 3) issue with some reduced sets of sigma points described in the literature; 4) the conservativeness of the SUT; 5) the scaling effect of the SUT on both its transformed covariance and cross-covariance matrices; and 6) possibly ill-conditioned results in SRUKF's. With the proposed systematization, the symmetric sets of sigma points in the literature are formally justified, and we are able to provide new consistent variations for UKF's, such as the Scaled SRUKF's and the UKF's composed by the minimum number of sigma points. Furthermore, our proposed SRUKF has improved computational properties when compared to state-of-the-art methods.

[1]  Fredrik Gustafsson,et al.  Some Relations Between Extended and Unscented Kalman Filters , 2012, IEEE Transactions on Signal Processing.

[2]  S. Särkkä,et al.  On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems , 2007, IEEE Transactions on Automatic Control.

[3]  Timothy J. Giese Numerical quadrature , .

[4]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[5]  Henrique Marra Menegaz,et al.  A new smallest sigma set for the Unscented Transform and its applications on SLAM , 2011, IEEE Conference on Decision and Control and European Control Conference.

[6]  Simo Särkkä,et al.  Bayesian Filtering and Smoothing , 2013, Institute of Mathematical Statistics textbooks.

[7]  Andrei Romanenko,et al.  The unscented filter as an alternative to the EKF for nonlinear state estimation: a simulation case study , 2004, Comput. Chem. Eng..

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

[9]  Simon J. Julier,et al.  The spherical simplex unscented transformation , 2003, Proceedings of the 2003 American Control Conference, 2003..

[10]  D. W. Arthur,et al.  Methods of numerical Integration (2nd edition), by Philip J. Davis and Philip Rabinowitz. Pp 612. £36·50. 1984. ISBN 0-12-206360-0 (Academic Press) , 1986, The Mathematical Gazette.

[11]  H.F. Durrant-Whyte,et al.  A new approach for filtering nonlinear systems , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[12]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[13]  Dirk P. Kroese,et al.  Handbook of Monte Carlo Methods , 2011 .

[14]  David W. Murray,et al.  An O(N²) Square Root Unscented Kalman Filter for Visual Simultaneous Localization and Mapping , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Fredrik Gustafsson,et al.  On nonlinear transformations of stochastic variables and its application to nonlinear filtering , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[16]  Peter Jäckel A note on multivariate Gauss-Hermite quadrature , 2005 .

[17]  Jeffrey K. Uhlmann,et al.  A consistent, debiased method for converting between polar and Cartesian coordinate systems , 1997 .

[18]  Rudolph van der Merwe,et al.  The square-root unscented Kalman filter for state and parameter-estimation , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[19]  Wei Ping,et al.  A novel simplex unscented transform and filter , 2007, 2007 International Symposium on Communications and Information Technologies.

[20]  Mónica F. Bugallo,et al.  Joint Model Selection and Parameter Estimation by Population Monte Carlo Simulation , 2010, IEEE Journal of Selected Topics in Signal Processing.

[21]  T. Singh,et al.  The higher order unscented filter , 2003, Proceedings of the 2003 American Control Conference, 2003..

[22]  Dan Simon,et al.  Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches , 2006 .

[23]  Simo Särkkä,et al.  Continuous-time and continuous-discrete-time unscented Rauch-Tung-Striebel smoothers , 2010, Signal Process..

[24]  Uri Lerner,et al.  Hybrid Bayesian networks for reasoning about complex systems , 2002 .

[25]  George P. Papavassilopoulos,et al.  Computationally Efficient Kalman Filtering for a Class of Nonlinear Systems , 2011, IEEE Transactions on Automatic Control.

[26]  H. Sorenson,et al.  Nonlinear Bayesian estimation using Gaussian sum approximations , 1972 .

[27]  Yong Wang,et al.  An improved square root unscented Kalman filter for projectile's attitude determination , 2010, 2010 5th IEEE Conference on Industrial Electronics and Applications.

[28]  Yan Li,et al.  Comparison of Extended and Unscented Kalman Filters applied to EEG signals , 2010, IEEE/ICME International Conference on Complex Medical Engineering.

[29]  Xiaobei Zhao,et al.  The square-root spherical simplex unscented Kalman filter for state and parameter estimation , 2008, 2008 9th International Conference on Signal Processing.

[30]  Mohinder S. Grewal,et al.  Kalman Filtering: Theory and Practice , 1993 .

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

[32]  B. Rozovskii,et al.  The Oxford Handbook of Nonlinear Filtering , 2011 .

[33]  Robert J. Elliott,et al.  Discrete-Time Nonlinear Filtering Algorithms Using Gauss–Hermite Quadrature , 2007, Proceedings of the IEEE.

[34]  George P. Papavassilopoulos,et al.  Kalman Filtering for a Generalized Class of Nonlinear Systems and a New Gaussian Quadrature Technique , 2012, IEEE Transactions on Automatic Control.

[35]  G. P. Papavassilopoulos,et al.  Development and numerical investigation of new non-linear Kalman filter variants , 2011 .

[36]  Alessio Benavoli,et al.  The Generalized Moment-Based Filter , 2013, IEEE Transactions on Automatic Control.

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

[38]  Simon J. Julier,et al.  The scaled unscented transformation , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

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

[40]  Ondrej Straka,et al.  Aspects and comparison of matrix decompositions in unscented Kalman filter , 2013, 2013 American Control Conference.

[41]  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..

[42]  Yuanxin Wu,et al.  Unscented Kalman filtering for additive noise case: augmented versus nonaugmented , 2005, IEEE Signal Processing Letters.

[43]  Neil J. Gordon,et al.  Editors: Sequential Monte Carlo Methods in Practice , 2001 .

[44]  Torsten Söderström,et al.  Advanced point-mass method for nonlinear state estimation , 2006, Autom..

[45]  Eric Kerherve,et al.  Doherty amplifier optimization using robust genetic algorithm and Unscented Transform , 2011, 2011 IEEE 9th International New Circuits and systems conference.

[46]  Ali Ihsan Hasçelik,et al.  Gauss quadrature rules for a generalized Hermite weight function , 2006, Appl. Math. Comput..

[47]  Jinlong Zhang,et al.  State-of-charge estimation of valve regulated lead acid battery based on multi-state Unscented Kalman Filter , 2011 .

[48]  J. Navarro-Pedreño Numerical Methods for Least Squares Problems , 1996 .

[49]  Joris De Schutter,et al.  Kalman filters for nonlinear systems , 2002 .

[50]  Simon Haykin,et al.  Square-Root Quadrature Kalman Filtering , 2008, IEEE Transactions on Signal Processing.

[51]  Philip Rabinowitz,et al.  Methods of Numerical Integration , 1985 .

[52]  Ondrej Straka,et al.  Unscented Kalman Filter: Aspects and Adaptive Setting of Scaling Parameter , 2012, IEEE Transactions on Automatic Control.

[53]  Jeffrey K. Uhlmann,et al.  Reduced sigma point filters for the propagation of means and covariances through nonlinear transformations , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[54]  Christoph Ament,et al.  The Unscented Kalman Filter estimates the plasma insulin from glucose measurement , 2011, Biosyst..

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

[56]  F. Daum Nonlinear filters: beyond the Kalman filter , 2005, IEEE Aerospace and Electronic Systems Magazine.

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

[58]  Simon J. Julier,et al.  Skewed approach to filtering , 1998, Defense, Security, and Sensing.

[59]  James C. Spa Estimation via Markov Chain Monte Carlo , 2002 .

[60]  Yuanxin Wu,et al.  A Numerical-Integration Perspective on Gaussian Filters , 2006, IEEE Transactions on Signal Processing.

[61]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[62]  Herman Bruyninckx,et al.  Comment on "A new method for the nonlinear transformation of means and covariances in filters and estimators" [with authors' reply] , 2002, IEEE Trans. Autom. Control..

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

[64]  Simo Srkk,et al.  Bayesian Filtering and Smoothing , 2013 .

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

[66]  Mohinder S. Grewal,et al.  Kalman Filtering: Theory and Practice Using MATLAB , 2001 .

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