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    Random finite set multi-target trackers: Stochastic geometry for space situational awareness

    Access Status
    Fulltext not available
    Authors
    Vo, Ba Tuong
    Vo, B.
    Date
    2015
    Type
    Conference Paper
    
    Metadata
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    Citation
    Vo, B.T. and Vo, B. 2015. Random finite set multi-target trackers: Stochastic geometry for space situational awareness, in Kadar, I. (ed), Proceedings of the Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV, April 20-22 2015. Baltimore: SPIE.
    Source Title
    Proceedings of SPIE - The International Society for Optical Engineering
    DOI
    10.1117/12.2180839
    ISBN
    9781628415902
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/46641
    Collection
    • Curtin Research Publications
    Abstract

    © 2015 SPIE. This paper describes the recent development in the random finite set RFS paradigm in multi-target tracking. Over the last decade the Probability Hypothesis Density filter has become synonymous with the RFS approach. As result the PHD filter is often wrongly used as a performance benchmark for the RFS approach. Since there is a suite of RFS-based multi-target tracking algorithms, benchmarking tracking performance of the RFS approach by using the PHD filter, the cheapest of these, is misleading. Such benchmarking should be performed with more sophisticated RFS algorithms. In this paper we outline the high-performance RFS-based multi-target trackers such that the Generalized Labled Multi-Bernoulli filter, and a number of efficient approximations and discuss extensions and applications of these filters. Applications to space situational awareness are discussed.

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