@inproceedings{0b6e8843030d4831a067a5f226664eac,
title = "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 this problem. 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.",
keywords = "Iterated extended Kalman smoother, Nonlinear estimation, Parallel computing, Sigma-point smoother",
author = "Fatemeh Yaghoobi and Adrien Corenflos and Sakira Hassan and Simo S{\"a}rkk{\"a}",
note = "Funding Information: The authors would like to thank Academy of Finland for funding. Publisher Copyright: {\textcopyright} 2021 IEEE.; IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP ; Conference date: 06-06-2021 Through 11-06-2021",
year = "2021",
doi = "10.1109/ICASSP39728.2021.9413364",
language = "English",
series = " Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing",
publisher = "IEEE",
pages = "5350--5354",
booktitle = "Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021",
address = "United States",
}