Distributed Kalman Filtering: Consensus, Diffusion, and Mixed

Sayed Pouria Talebi, Stefan Werner

    Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

    12 Sitaatiot (Scopus)

    Abstrakti

    A distributed Kalman filtering technique is developed for tracking state-space processes via sensor networks. Considering the optimal solution to multi-agent sequential filtering of linear Gaussian state-space processes, that is the centralized Kalman filter, this work focuses on decomposing and distributing the operation of the centralized Kalman filter among the agents of the sensor network. This decomposition is performed in a fashion that allows each agent to maintain a local Kalman filtering operation and an intermediate estimate of the state vector, providing for a robust distributed Kalman filtering technique that is scalable with the size of the network. In contrast to state-of-the-art distributed Kalman filtering approaches that focus on the use of consensus or diffusion as the basis of their information fusion, a mixed approach is proposed that exploits advantages of both methods. The performance of the proposed distributed Kalman filtering technique is verified in a simulation example, where the proposed technique is shown to outperform state-of-the-art distributed Kalman filtering algorithms.

    AlkuperäiskieliEnglanti
    Otsikko2018 IEEE Conference on Control Technology and Applications, CCTA 2018
    KustantajaIEEE
    Sivut1126-1132
    Sivumäärä7
    ISBN (elektroninen)9781538676981
    DOI - pysyväislinkit
    TilaJulkaistu - 26 lokak. 2018
    OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
    TapahtumaIEEE Conference on Control Technology and Applications - Copenhagen, Tanska
    Kesto: 21 elok. 201824 elok. 2018
    Konferenssinumero: 2

    Conference

    ConferenceIEEE Conference on Control Technology and Applications
    LyhennettäCCTA
    Maa/AlueTanska
    KaupunkiCopenhagen
    Ajanjakso21/08/201824/08/2018

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