This paper presents the development of a tracking algorithm for multi-sensor single target tracking in the presence of asynchronous or missing measurements and high clutter levels. The algorithm is based upon the random sample representation of state PDFs and uses sequential Monte Carlo or "particle" filtering methods to perform prediction and update. The performance of the algorithm is illustrated on the challenging problem of naval subsurface target tracking using multiple drifting sonobuoys of the DIFAR type. Good performance was demonstrated on simulated scenarios with a high level of uncertainty represented by unknown sensor location, 20% missing measurements and 70% clutter.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Asynchronous multi-sensor tracking in clutter with uncertain sensor locations using Bayesian sequential Monte Carlo methods


    Contributors:
    Marrs, A.D. (author)


    Publication date :

    2001-01-01


    Size :

    538768 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Sequential Dynamic Leadership Inference Using Bayesian Monte Carlo Methods

    Li, Qing / Ahmad, Bashar I. / Godsill, Simon J. | IEEE | 2021


    Fusion of laser and radar sensor data with a sequential Monte Carlo Bayesian occupancy filter

    Nuss, Dominik / Yuan, Ting / Krehl, Gunther et al. | IEEE | 2015


    Sequential Monte Carlo Filtering for Multi-Aspect Detection/Tracking

    Bruno, M. G. S. / de Arajo, R. V. / Pavlov, A. G. et al. | British Library Conference Proceedings | 2005


    Sequential Fusion for Multi-sensor Asynchronous Sampling System

    Peng, D. / Ye, J. / Ge, Q. | British Library Online Contents | 2010