Lslock: A method to estimate state space model by spatiotemporal continuity

Tsuyoshi Ishizone, Kazuyuki Nakamura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Model estimation from spatio-temporal data is important topic since it helps us to extract useful information from big data in recent years. In this paper, we introduce an estimation algorithm of the linear Gaussian state space model with focusing on the real-time property. The proposed algorithm uses two key ideas, localization and spatial uniformity, to reduce the number of the parameters. Thanks to this, we obtain stable method to estimate the parameters regarding state transition and states. In addition, the proposed algorithm is quicker and more accurate than existing methods, therefore, it suffices the requirement of the rapid response for the alternation of the fields.

Original languageEnglish
Title of host publicationCONTROLO 2020 - Proceedings of the 14th APCA International Conference on Automatic Control and Soft Computing
EditorsJosé Alexandre Gonçalves, Manuel Braz-César, João Paulo Coelho
PublisherSpringer Science and Business Media Deutschland GmbH
Pages342-351
Number of pages10
ISBN (Print)9783030586522
DOIs
Publication statusPublished - 2021
Event14th APCA International Conference on Automatic Control and Soft Computing, CONTROLO 2020 - Bragança, Portugal
Duration: 1 Jul 20203 Jul 2020

Publication series

NameLecture Notes in Electrical Engineering
Volume695 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference14th APCA International Conference on Automatic Control and Soft Computing, CONTROLO 2020
CountryPortugal
CityBragança
Period1/07/203/07/20

Keywords

  • Kalman filter
  • Noise reduction
  • Online learning
  • Real-time calculation
  • Short-term prediction

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