Research on Attitude and Position Tracking Algorithm of Drill Bit for Underwater Mud-Penetrator

Abstract:

This paper proposed a navigation algorithm based on indirect extended kalman filter. The algorithm adopted state error model based on sensor random walk and chose low frequency value of acceleration and angular velocity as observation. The extended kalman filter was used to calculate the optimal estimation of state error which can be used to correct the predicted value of state. IMU7200 and encoder provide acceleration, angular velocity and velocity for drill bit. Marine experiments in the East China Sea show that the proposed algorithm can successfully implement with an accurate and reliable performance.

• Periodical: Advanced Materials Research (Volumes 591-593)
• Pages: 1381-1385
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.591-593.1381
• Online since: November 2012
• Authors: Ke Yang, Tong Ge, Xu Yang Wang
• Keywords: Indirect Extended Kalman Filter, Low Frequency Value, Random Walk Model, State Error

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