On the "near-universal proxy" argument for theoretical justification of information-driven sensor management

E.H. Aoki, Arunabha Bagchi, Pranab K. Mandal, Y. Boers

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    5 Citations (Scopus)


    In sensor management applications, sometimes it may be difficult to find a goal function that meaningfully represents the desired qualities of the estimate, such as when we do not have a clear performance metric or when the computation cost of the goal function is prohibitive. An alternative is to use goal functions based on information theory, such as the Rényi divergence (also called $\alpha$-divergence). One strong argument in favor of information-driven sensor management is that the Rényi divergence is a "near-universal" proxy for arbitrary task-driven risk functions, implying that these could be replaced by a Rényi divergence-based criterion, and this would usually result in satisfactory performance. In this paper, we present a rebuttal to that argument, which implies that finding theoretical justification for information-driven sensor management still seems to be an open problem.
    Original languageUndefined
    Title of host publicationIEEE Statistical Signal Processing Workshop (SSP 2011)
    Place of PublicationUSA
    Number of pages4
    ISBN (Print)978-1-4577-0569-4
    Publication statusPublished - 28 Jun 2011
    EventIEEE Statistical Signal Processing Workshop, SSP 2011 - Nice, France
    Duration: 28 Jun 201130 Jun 2011

    Publication series

    PublisherIEEE Signal Processing Society


    WorkshopIEEE Statistical Signal Processing Workshop, SSP 2011
    Other28-30 June 2011


    • METIS-286295
    • Rényi divergence
    • Sensor management
    • IR-79954
    • EWI-21682
    • Information theory

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