Particle filter based entropy

Yvo Boers, Hans Driessen, Arunabha Bagchi, Pranab Mandal

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

    19 Citations (Scopus)
    65 Downloads (Pure)


    For many problems in the field of tracking or even the wider area of filtering the a posteriori description of the uncertainty can oftentimes not be described by a simple Gaussian density function. In such situations the characterization of the uncertainty by a mean and a covariance does not capture the true extent of the uncertainty at hand. For example, when the posterior is multi-modal with well separated narrow modes. Such descriptions naturally occur in applications like target tracking with terrain constraints or tracking of closely spaced multiple objects, where one cannot keep track of the objects identities. In such situations a covariance measure as a description of the uncertainty is not appropriate anymore. In this paper we look at the use of entropy as an uncertainty description. We show how to calculate the entropy based on a running particle filter. We will verify the particle based approximation of the entropy numerically. We we also discuss theoretical convergence properties and provide some motivating examples.
    Original languageEnglish
    Title of host publicationProceedings of the 13th International Conference on Information Fusion, FUSION 2010
    PublisherThe Institution of Engineering and Technology
    Number of pages8
    ISBN (Print)978-0-9824438-1-1
    Publication statusPublished - Jul 2010
    Event13th International Conference on Information Fusion, FUSION 2010 - Edinburgh, United Kingdom
    Duration: 26 Jul 201029 Jul 2010
    Conference number: 13


    Conference13th International Conference on Information Fusion, FUSION 2010
    Abbreviated titleFUSION 2010
    CountryUnited Kingdom


    • Multi-object tracking
    • Particle filter
    • Entropy

    Fingerprint Dive into the research topics of 'Particle filter based entropy'. Together they form a unique fingerprint.

    Cite this