Parallel Iterated Extended and Sigma-point Kalman Smoothers
Abstract
The problem of Bayesian filtering and smoothing in nonlinear models with additive noise is an active area of research. Classical Taylor series as well as more recent sigma-point based methods are two well-known strategies to deal with these problems. However, these methods are inherently sequential and do not in their standard formulation allow for parallelization in the time domain. In this paper, we present a set of parallel formulas that replace the existing sequential ones in order to achieve lower time (span) complexity. Our experimental results done with a graphics processing unit (GPU) illustrate the efficiency of the proposed methods over their sequential counterparts.
Cite
@article{arxiv.2102.00514,
title = {Parallel Iterated Extended and Sigma-point Kalman Smoothers},
author = {Fatemeh Yaghoobi and Adrien Corenflos and Sakira Hassan and Simo Särkkä},
journal= {arXiv preprint arXiv:2102.00514},
year = {2021}
}
Comments
Accepted to be published in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2021