Unscented Rauch--Tung--Striebel Smoother

This note considers the application of the unscented transform to optimal smoothing of nonlinear state-space models. In this note, a new Rauch-Tung-Striebel type form of the fixed-interval unscented Kalman smoother is derived. The new smoother differs from the previously proposed two-filter-formulation-based unscented Kalman smoother in the sense that it is not based on running two independent filters forward and backward in time. Instead, a separate backward smoothing pass is used, which recursively computes corrections to the forward filtering result. The smoother equations are derived as approximations to the formal Bayesian optimal smoothing equations. The performance of the new smoother is demonstrated with a simulation.

[1]  N E Manos,et al.  Stochastic Models , 1960, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[2]  N. Rashevsky,et al.  Mathematical biology , 1961, Connecticut medicine.

[3]  R. E. Kalman,et al.  New Results in Linear Filtering and Prediction Theory , 1961 .

[4]  Y. Ho,et al.  A Bayesian approach to problems in stochastic estimation and control , 1964 .

[5]  D. Fraser,et al.  The optimum linear smoother as a combination of two optimum linear filters , 1969 .

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

[7]  Abderrahmane Haddad,et al.  Estimation theory with applications to communications and control , 1972 .

[8]  William L. Brogan Applied Optimal Estimation (Arthur Gels, ed.) , 1977 .

[9]  F. Lewis Optimal Estimation: With an Introduction to Stochastic Control Theory , 1986 .

[10]  Robert F. Stengel,et al.  Optimal Control and Estimation , 1994 .

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

[12]  G. Kitagawa Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[13]  S. Julier,et al.  A General Method for Approximating Nonlinear Transformations of Probability Distributions , 1996 .

[14]  John Weston,et al.  Strapdown Inertial Navigation Technology , 1997 .

[15]  Peter J. W. Rayner,et al.  Digital Audio Restoration: A Statistical Model Based Approach , 1998 .

[16]  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..

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

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

[19]  Rudolph van der Merwe,et al.  The Unscented Kalman Filter , 2002 .

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

[21]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[22]  E. Somersalo,et al.  Statistical and computational inverse problems , 2004 .

[23]  Nando de Freitas,et al.  Fast particle smoothing: if I had a million particles , 2006, ICML.

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

[25]  M. Melamed Detection , 2021, SETI: Astronomy as a Contact Sport.