Terminal homing position estimation for Autonomous Underwater Vehicle docking
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This study aims at developing an improved methodology for position estimation by which sparse, erroneous and inconsistent sensor observations can be utilized for Autonomous Underwater Vehicle (AUV) terminal homing to an undersea docking station. An undersea docking station offers great potential for increasing AUV at-sea time and reducing survey costs. A key part of this system is the ability of the AUV to successfully dock. Part of the process for successful docking is AUV position estimation. The Digital Ultra-Short Baseline (D-USBL) is a sensor system used by the AUV to provide range and bearing measurements to the docking station. These measurements can be used by the AUV to improve its position estimate. Due to the nonlinearity of the D-USBL measurements, the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and forward and backward smoothing (FBS) filter were utilized to estimate the position of the AUV. After performance of these filters was deemed unsatisfactory, a new smoothing technique called the Moving Horizon Estimation (MHE) with epi-splines was introduced. The MHE smoothing filter used the dead reckoning measurements and the D-USBL measurements as constraints in the epi-splines optimization method. The analysis based on data sets of REMUS 100 AUV docking station runs was conducted using the MHE/epi-splines methodology and compared to EKF and UKF algorithms. The MHE/epi-splines algorithm demonstrated significantly better performance over the EKF and UKF.
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