ANFIS-EKF-Based Single-Beacon Localization Algorithm for AUV

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 20; p. 5281
• Main Authors: Zhao, Wanlong, Zhao, Huifeng, Liu, Gongliang, Zhang, Guoyao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: Acoustics; Algorithms; ANFIS; Autonomous underwater vehicles; AUV; Deep learning; EKF; Extended Kalman filter; Field tests; Localization; observability analysis; Remote sensing; Sensors; single-beacon localization; Teaching methods; Tracking problem; Underwater vehicles; Velocity
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14205281

Singe-beacon localization technology can help Autonomous Underwater Vehicles (AUVs) to obtain precise positions by deploying only one beacon. It is considered as a promising way, benefiting from saving much time and labor compared with traditional Long-Baseline Localization (LBL). A typical single-beacon localization scheme contains two essential questions: the initial observability problem and long-endurance trajectory tracking problem. Aiming at these core problems, a comprehensive solution for single-beacon localization is described in this paper. An multi-hypothesis initial position discriminant method is proposed firstly, which helps to achieve accurate initial location based on observability analysis. Then, an Adaptive Network Fuzzy Inference System (ANFIS)-improved Extended Kalman Filter (EKF) method is proposed, in which single-beacon measuring information is fused with off-the-shelf sensors, including DVL, Compass, etc. ANFIS-EKF can help to improve trajectory tracking precisions by restraining the heavy loss of linearization in conventional EKF. Both simulation and field tests are conducted to verify the performance of the proposed algorithms.