The 7th IFAC Symposium on Intelligent Autonomous Vehicles 2010

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Parameter Estimation of an AUV Using the Maximum Likelihood method and a Kalman Filter with Fading Memory

Ehsan Shahinfar
Yazd University
Iran

Mohammad Bozorg
Yazd University
Iran

Mohsen Bidoky
Amirkabir University of technology
Iran

Abstract:
The aim of this paper is to estimate the unknown hydrodynamic coefficients of an AUV. Discrete Kalman filter with fading memory is implemented to estimate the state variables of system and Maximum likelihood is used for the parameter estimation, and with a damped Newton method the cost function is optimized. Both process and measurement noises are assumed. To check the accuracy of the algorithm a model of SNUUV-I is used. The dynamic of AUV in the diving mode is identified. It is observed that the algorithm of parameter estimation is accurate and by using Kalman filter with fading memory and damped Newton method instead of the regular Kalman Filter, the accuracy of the results has improved.

 

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