The application of AUV navigation based on adaptive extended Kalman filter

An effective and accurate navigation algorithm is the critical to achieve the purpose of precise localization and autonomous navigation for Autonomous Underwater Vehicle (AUV). Extended Kalman Filter (EKF) is one of the most popular methods, because it is easy to implement. In spite of its wide applications, it still suffers from two major drawbacks as follows: (1) the noise covariance matrixes are difficult to be determined, and (2) the fixed noise covariance matrixes of EKF are hard to satisfy all situations. To avoid such disadvantages, this paper applies Adaptive Extended Kalman Filter (AEKF) algorithm to AUV, which can adaptively adjust the measurement covariance matrix and the process noise covariance matrix by utilizing latest measurement data. The feasibility of AUV navigation based on AEKF is verified by a large number of sea trials on own platform, Sailfish, in Tuandao Bay (Qingdao). Besides, the experimental results show that the control effect and navigation accuracy of our AUV based on AEKF are higher than EKF and Dead Reckoning (DR) in real-world experiment.