Particle Filter ROV Navigation using Hydrodynamic Position and Speed log Measurements

Bo Zhao, Mogens Blanke, Roger Skjetne

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    Abstract

    An integrated navigation system design is presented for an underwater remotely operated vehicle (ROV). The available navigation information is an acoustic position measurement and a Doppler log speed measurement. Both measurements are studied in detail and modeled statistically. A kinematic model is assigned to the ROV with its driving noise from a Gaussian mixture, and a particle filter is suggested to estimate ROV position and velocity. The advantages of using a particle filter in this ROV navigation scheme are: 1) to make full use of all available information to improve the estimation performance, such as the speed measurement that is a nonlinear function of the states; 2) the particle filter makes good use of a Gaussian mixture as the driving noise, which makes the ROV kinematic model more realistic in both high and low frequency ranges; 3) a good estimate of the ROV velocity vector is achieved. The algorithm of the particle filter is presented and verified through a simulation based on real data. This shows that the estimation performance of the particle filter is clearly better than that of a Kalman filter.
    Original languageEnglish
    Title of host publicationProceedings of the 2012 American Control Conference
    Publication date2012
    Pages6209 - 6215
    ISBN (Print)978-1-4577-1094-0
    Publication statusPublished - 2012
    EventAmerican Control Conference (ACC 2012) - Fairmont Queen Elizabeth, Montréal, Canada
    Duration: 27 Jun 201229 Jun 2012
    http://a2c2.org/conferences/acc2012/index.php

    Conference

    ConferenceAmerican Control Conference (ACC 2012)
    LocationFairmont Queen Elizabeth
    Country/TerritoryCanada
    CityMontréal
    Period27/06/201229/06/2012
    Internet address

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