Parallel iterated extended and sigma-point Kalman smoothers

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

5 Sitaatiot (Scopus)

Abstrakti

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.

AlkuperäiskieliEnglanti
OtsikkoProceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021
KustantajaIEEE
Sivut5350-5354
Sivumäärä5
ISBN (elektroninen)978-1-7281-7605-5
DOI - pysyväislinkit
TilaJulkaistu - 2021
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Conference on Acoustics, Speech, and Signal Processing - Virtua, Online, Toronto, Kanada
Kesto: 6 kesäk. 202111 kesäk. 2021

Julkaisusarja

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

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
LyhennettäICASSP
Maa/AlueKanada
KaupunkiToronto
Ajanjakso06/06/202111/06/2021

Sormenjälki

Sukella tutkimusaiheisiin 'Parallel iterated extended and sigma-point Kalman smoothers'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä