Noisy network localization via optimal measurement refinement part 1: Bearing-only orientation registration and localization

Adrian N. Bishop*, Iman Shames

*Corresponding author for this work

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

    6 Citations (Scopus)

    Abstract

    The problem of localizing or tracking a number of targets using a network of bearing-only sensors is considered. To solve such a high-level problem, each sensor report must be successfully recorded in a common spatial reference frame and the position of the sensors must be determined. In practice, however, the reports from individual sensors are characterized by both random (called noise) and systematic errors (called biases). Typical bias errors are axis misalignments (due to azimuth and elevation biases) and range offset errors. Conditions under which the systematic errors can be removed given noisy measurements are examined in this work. In addition, certain conditions are identified which lend themselves naturally to the design of algorithms for network registration, localization and subsequently target localization. These conditions are feasible from a computational complexity point of view. This work provides a comprehensive solution to the problem of sensor network-based target localization with bearing measurements as very little a prior information is assumed known and, if certain sensing conditions are met, efficient algorithms are provided.

    Original languageEnglish
    Title of host publicationProceedings of the 18th IFAC World Congress
    PublisherIFAC Secretariat
    Pages8842-8847
    Number of pages6
    Edition1 PART 1
    ISBN (Print)9783902661937
    DOIs
    Publication statusPublished - 2011

    Publication series

    NameIFAC Proceedings Volumes (IFAC-PapersOnline)
    Number1 PART 1
    Volume44
    ISSN (Print)1474-6670

    Fingerprint

    Dive into the research topics of 'Noisy network localization via optimal measurement refinement part 1: Bearing-only orientation registration and localization'. Together they form a unique fingerprint.

    Cite this