Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Multitarget tracking with multiscan knowledge exploitation using sequential MCMC sampling

    Access Status
    Fulltext not available
    Authors
    Bocquel, M.
    Papi, Francesco
    Podt, M.
    Driessen, H.
    Date
    2013
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Bocquel, M. and Papi, F. and Podt, M. and Driessen, H. 2013. Multitarget tracking with multiscan knowledge exploitation using sequential MCMC sampling. IEEE Journal on Selected Topics in Signal Processing. 7 (3): pp. 532-542.
    Source Title
    IEEE Journal on Selected Topics in Signal Processing
    DOI
    10.1109/JSTSP.2013.2251317
    ISSN
    1932-4553
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/38605
    Collection
    • Curtin Research Publications
    Abstract

    Exploitation of external knowledge through constrained filtering guarantees improved performance. In this paper we show how multiscan processing of such information further enhances the track accuracy. This can be achieved using a Fixed-Lag Smoothing procedure, and a proof of improvement is given in terms of entropy reduction. Such multiscan algorithm, i.e., named KB-Smoother ('Fixed-lag smoothing for Bayes optimal exploitation of external knowledge,' F. Papi , Proc. 15th Int. Conf. Inf. Fusion, 2012) can be implemented by means of a SIR-PF. In practice, the SIR-PF suffers from depletion problems, which are further amplified by the Smoothing technique. Sequential MCMC methods represent an efficient alternative to the standard SIR-PF approach. Furthermore, by borrowing techniques from genetic algorithms, a fully parallelizable multitarget tracker can be defined. Such approach, i.e., named Interacting Population (IP)-MCMC-PF, was first introduced in 'Multitarget tracking with interacting population-based MCMC-PF' (M Bocquel , Proc. 15th Int. Conf. Inf. Fusion, 2012). In this paper, we propose and analyze a combination of the KB-Smoother along with the IP-MCMC-PF. As will be shown, the combination of the two methods yields an improved track accuracy while mitigating the loss of particles diversity. Simulation analyses for single and multitarget tracking scenarios confirm the benefits of the proposed approach. © 2007-2012 IEEE.

    Related items

    Showing items related by title, author, creator and subject.

    • MCMC-based posterior independence approximation for RFS multitarget particle filters
      García-Fernández, A.; Vo, Ba-Ngu; Vo, Ba Tuong (2014)
      The objective of this paper is to approximate the unlabelled posterior random finite set (RFS) density in multitarget tracking (MTT) using particle filters (PFs). The unlabelled posterior can be equivalently represented ...
    • An introduction to force and measurement modeling for space object tracking
      Mallick, M.; Rubin, S.; Vo, Ba-Ngu (2013)
      Space object (satellite or space-debris) tracking (SOT) has not received much attention in the Information Fusion community, although the first Fusion conference was held in 1998. A special session on SOT was organized ...
    • 'Statistics 102' for multisource-multitarget detection and tracking
      Mahler, Ronald (2013)
      This tutorial paper summarizes the motivations, concepts and techniques of finite-set statistics (FISST), a system-level, 'top-down,' direct generalization of ordinary single-sensor, single-target engineering statistics ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.