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

    Sensor management for multi-target tracking via multi-bernoulli filtering

    222096_222096.pdf (251.0Kb)
    Access Status
    Open access
    Authors
    Hoang, Hung
    Vo, Ba Tuong
    Date
    2014
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Hoang, H. and Vo, B.T. 2014. Sensor management for multi-target tracking via multi-bernoulli filtering. Automatica. 50 (4): pp. 1135-1142.
    Source Title
    Automatica
    DOI
    10.1016/j.automatica.2014.02.007
    ISSN
    0005-1098
    School
    Department of Electrical and Computer Engineering
    Remarks

    NOTICE: this is the author’s version of a work that was accepted for publication in Automatica. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Automatica, Vol. 50. no 4 (2014). DOI: 10.1016/j.automatica.2014.02.007

    URI
    http://hdl.handle.net/20.500.11937/21965
    Collection
    • Curtin Research Publications
    Abstract

    In multi-object stochastic systems, the issue of sensor management is a theoretically and computationally challenging problem. In this paper, we present a novel random finite set (RFS) approach to the multi-target sensor management problem within the partially observed Markov decision process (POMDP) framework. The multi-target state is modelled as a multi-Bernoulli RFS, and the multi-Bernoulli filter is used in conjunction with two different control objectives: maximizing the expected Rényi divergence between the predicted and updated densities, and minimizing the expected posterior cardinality variance. Numerical studies are presented in two scenarios where a mobile sensor tracks five moving targets with different levels of observability.

    Related items

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

    • Multi-Bernoulli filter for target tracking with multi-static Doppler only measurement
      Liang, M.; Kim, Du Yong; Kai, X. (2015)
      Multi-static Doppler-shift has re-emerged recently in the target tracking literature along with passive sensing, especially for aircraft tracking. Tracking with multi-static Doppler only measurement requires efficient ...
    • Multi-Bernoulli filtering with unknown clutter intensity and sensor field-of-view
      Vo, Ba Tuong; Vo, Ba-Ngu; Hoseinnezhad, R.; Mahler, R. (2011)
      In Bayesian multi-target filtering knowledge of parameters such as clutter intensity and sensor field-of-view are of critical importance. Significant mismatches in clutter and sensor field of view model parameters results ...
    • An Adaptive Multi-Sensor Generalised Labelled Multi-Bernoulli Filter for Linear Gaussian Models
      Nguyen, Tran Thien Dat ; Do, C.T.; Nguyen, Hoa Van (2022)
      Recent development of the multi-sensor generalised labelled multi-Bernoulli (MS-GLMB) tracking algorithm allows joint estimation of target trajectories adjunct to clutter rate and detection probability. Nevertheless, it ...
    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.