Iterated Posterior Linearization Smoother

Angel F. Garcia-Fernandez, Lennart Svensson, Simo Sarkka
2017 IEEE Transactions on Automatic Control  
This paper considers the problem of Bayesian smoothing in nonlinear state-space models with additive noise using Gaussian approximations. Sigma-point approximations to the general Gaussian Rauch-Tung-Striebel smoother are widely used methods to tackle this problem. These algorithms perform statistical linear regression (SLR) of the nonlinear functions considering only the previous measurements. We argue that SLR should be done taking all measurements into account. We propose the iterated
more » ... the iterated posterior linearisation smoother (IPLS), which is an iterated algorithm that performs SLR of the nonlinear functions with respect to the current posterior approximation. The algorithm is demonstrated to outperform conventional Gaussian nonlinear smoothers in two numerical examples.
doi:10.1109/tac.2016.2592681 fatcat:k2g5awjttjgh7h6eftuxgpzymq