Bias reduction based on maximum likelihood estimates with application in scan-based localization

Yiming Ji, Changbin Yu, Brian D.O. Anderson

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

    Abstract

    In this paper, a novel bias reduction method is proposed to analytically express and reduce the bias arising in localization problems, thereby improving the localization accuracy. The proposed bias reduction method mixes Taylor series and a maximum likelihood estimate, and leads to an easily calculated analytical bias expression in terms of a known maximum likelihood cost function. In the simulations we apply the proposed method to the scan-based localization problem. Monte Carlo simulation results demonstrate the performance of the proposed method in this context.

    Original languageEnglish
    Title of host publicationProceedings of the 32nd Chinese Control Conference, CCC 2013
    PublisherIEEE Computer Society
    Pages7371-7376
    Number of pages6
    ISBN (Print)9789881563835
    Publication statusPublished - 18 Oct 2013
    Event32nd Chinese Control Conference, CCC 2013 - Xi'an, China
    Duration: 26 Jul 201328 Jul 2013

    Publication series

    NameChinese Control Conference, CCC
    ISSN (Print)1934-1768
    ISSN (Electronic)2161-2927

    Conference

    Conference32nd Chinese Control Conference, CCC 2013
    Country/TerritoryChina
    CityXi'an
    Period26/07/1328/07/13

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