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    Bayesian Sequential Track Formation

    225291_225291.pdf (1.735Mb)
    Access Status
    Open access
    Authors
    Garcia Fernandez, Angel
    Morelande, M.
    Grajal, J.
    Date
    2014
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Garcia Fernandez, A. and Morelande, M. and Grajal, J. 2014. Bayesian Sequential Track Formation. IEEE Transactions on Signal Processing. 62 (24): pp. 6366-6379.
    Source Title
    IEEE Transactions on Signal Processing
    DOI
    10.1109/TSP.2014.2364013
    ISSN
    1053-587X
    School
    Department of Electrical and Computer Engineering
    Remarks

    Copyright © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    URI
    http://hdl.handle.net/20.500.11937/21419
    Collection
    • Curtin Research Publications
    Abstract

    This paper presents a theoretical framework for track building in multiple-target scenarios from the Bayesian point of view. It is assumed that the number of targets is fixed and known. We propose two optimal methods for building tracks sequentially. The first one uses the labelling of the current multitarget state estimate that minimizes the mean-square labeled optimal subpatternassignment error. This method requires knowledge of the posterior density of the vector-valued state. The second assigns the labeling that maximizes the probability that the current multi-targetstate estimate is optimally linked with the available tracks at the previous time step. In this case, we only require knowledge of the random finite-set posterior density without labels.

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