A data fusion algorithm for multisensor systems

Research output: Chapter in Book/Report/Conference proceedingChapter

24 Citations (Scopus)

Abstract

The new data fusion algorithm presented in this paper allows one to combine information from different sensors in continuous time. Continuous-time decentralized Kalman filters (DKF) are used as data fusion devices on local subsystems. Such a structure gives the flexibility for reconfiguration of a control system. New subsystems can easily be added without needing any redesign of the whole system. The system does not require a central processor and therefore, in the case of failure of local subsystems (each of which includes a local processor, sensors and actuators) the overall system will continue to work. The simulation results show that the performance of the overall system degrades gracefully even if the sensors of subsystems fail or interconnections are broken. Furthermore, local Kalman filters can effectively reduce subsystem and measurement noise.
Original languageEnglish
Title of host publicationProceedings of the Fifth International Conference on Information Fusion, 2002
PublisherIEEE
Pages341-345
Volume1
ISBN (Print)0-9721844-1-4
DOIs
Publication statusPublished - 2002

Bibliographical note

This paper is available online at: http://fusion.isif.org/proceedings/fusion02CD/pdffiles/papers/M4C04.pdf?. It was given at the Fifth International Conference on Information Fusion, 2002 8-11 July, Annapolis, MD, USA

Keywords

  • continuous-time decentralized Kalman filters
  • control system reconfiguration
  • data fusion algorithm
  • measurement noises
  • multisensor systems
  • simulation
  • subsystem noise

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  • Cite this

    Vershinin, Y. (2002). A data fusion algorithm for multisensor systems. In Proceedings of the Fifth International Conference on Information Fusion, 2002 (Vol. 1, pp. 341-345). IEEE. https://doi.org/10.1109/ICIF.2002.1021172