Particle Filter ROV Navigation using Hydrodynamic Position and Speed log Measurements

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012


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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
StatePublished - 2012
EventAmerican Control Conference (ACC 2012) - Montréal, Canada


ConferenceAmerican Control Conference (ACC 2012)
LocationFairmont Queen Elizabeth
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