Parallel iterated extended and sigma-point Kalman smoothers

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021
PublisherIEEE
Pages5350-5354
Number of pages5
ISBN (Electronic)978-1-7281-7605-5
DOIs
Publication statusPublished - 2021
MoE publication typeA4 Conference publication
EventIEEE International Conference on Acoustics, Speech, and Signal Processing - Virtua, Online, Toronto, Canada
Duration: 6 Jun 202111 Jun 2021

Publication series

Name Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
ISSN (Print)1520-6149

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP
Country/TerritoryCanada
CityToronto
Period06/06/202111/06/2021

Keywords

  • Iterated extended Kalman smoother
  • Nonlinear estimation
  • Parallel computing
  • Sigma-point smoother

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