Particle Filter ROV Navigation using Hydrodynamic Position and Speed log Measurements

Bo Zhao, Mogens Blanke, Roger Skjetne

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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
Publication statusPublished - 2012
EventAmerican Control Conference (ACC 2012) - Fairmont Queen Elizabeth, Montréal, Canada
Duration: 27 Jun 201229 Jun 2012


ConferenceAmerican Control Conference (ACC 2012)
LocationFairmont Queen Elizabeth
Internet address

Cite this

Zhao, B., Blanke, M., & Skjetne, R. (2012). Particle Filter ROV Navigation using Hydrodynamic Position and Speed log Measurements. In Proceedings of the 2012 American Control Conference (pp. 6209 - 6215)