A data fusion algorithm for multisensor systems

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

    28 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

    Fingerprint

    Dive into the research topics of 'A data fusion algorithm for multisensor systems'. Together they form a unique fingerprint.

    Cite this