Multi-hypothesis tracking (MHT) techniques can become prohibitively computationally expensive as the number of hypotheses increases. In order to maintain an estimate with bounded computational cost, multi-hypothesis methods often merge the estimates together. When the hypotheses are distributed according to a known probability then standard mixture reduction (SMR) methods exist for merging estimates. Also, covariance union (CU) has become a popular approach to merging hypotheses when their distribution is not known. This paper generalises CU to a new theory, which we refer to as generalised covariance union (GCU). GCU merges estimates when their distribution is not known precisely but is, instead, bounded above and below. We show that CU and the SMR approaches are limiting cases of GCU. We demonstrate the efficacy of the new approach via a Global Positioning System (GPS) tracking application with time delayed satellite signals.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Generalised Covariance Union: A Unified Approach to Hypothesis Merging in Tracking


    Contributors:
    Reece, S. (author) / Roberts, S. (author)


    Publication date :

    2010-01-01


    Size :

    2040947 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    A unified approach to the vehicle-merging problem

    Athans, Michael | Elsevier | 1968



    An equivalence-class approach to multiple-hypothesis tracking

    Coraluppi, Stefano / Carthel, Craig | IEEE | 2012


    TRACKER: a sensor fusion simulator for generalised tracking

    Okello, N.N. / Tang, D. / McMichael, D.W. | Tema Archive | 1999