Smoothed State Estimation via Efficient Solution of Linear Equations

Abstract

This paper addresses the problem of computing fixed-interval smoothed state estimates of a linear time varying Gaussian stochastic system. There already exist many algorithms that perform this computation, but all of them impose certain restrictions on system matrices in order for them to be applicable, and the restrictions vary considerably between the various existing algorithms. This paper establishes a new sufficient condition for the fixed-interval smoothing density to exist in a Gaussian form that can be completely characterised by associated means and covariances. It then develops an algorithm to compute these means and covariances with no further assumptions required. This results in an algorithm more generally applicable than any one of the multitude of existing algorithms available to date.