A Low-Cost Dead Reckoning Navigation System for an AUV Using a Robust AHRS: Design and Experimental Analysis

TLDR

Attitude and heading estimation is critical for autonomous vehicle navigation, especially when GPS is unavailable and dead‑reckoning strategies are employed. The study designs a low‑cost, model‑aided dead‑reckoning navigation system for an AUV that uses an EKF‑based AHRS with MEMS inertial sensors. An EKF algorithm estimates attitude, heading, and gyroscope bias while compensating for external accelerations and magnetic disturbances, and is validated experimentally on an AUV under varying acceleration and magnetic conditions. The system achieves mean roll, pitch, and heading errors of 0.35°, 0.29°, and 1.27°, and a relative navigation error of about 8 % of the traveled distance during GPS outages.

Abstract

In navigation and motion control of an autonomous vehicle, estimation of attitude and heading is an important issue, especially when the localization sensors such as global positioning system (GPS) are not available and the vehicle is navigated by the dead reckoning (DR) strategies. In this paper, based on a new modeling framework, an extended Kalman filter (EKF) is utilized for estimation of attitude, heading, and gyroscope sensor bias using a low-cost microelectromechanical system (MEMS) inertial sensor. The algorithm is developed for accurate estimation of attitude and heading in the presence of external disturbances including external body accelerations and magnetic disturbances. In this study, using the proposed attitude and heading reference system (AHRS) and an identified surge dynamic model of an autonomous underwater vehicle, a low-cost model-aided DR navigation system has been designed for an AUV. The proposed algorithm application is evaluated by experimental tests in different acceleration bound and existence of external magnetic disturbances for an AUV. The results indicate that the roll, pitch, and heading are estimated by mean value errors about 0.35°, 0.29°, and 1.27°, respectively. Moreover, they indicate that a relative navigation error about 8% of the traveling distance can be achieved using the developed approach in during GPS outages.