High-precision, limited-beacon-aided AUV localization algorithm

Although the conventional long baseline (LBL) positioning system has high positioning accuracy, it does not properly account for the motion of autonomous underwater vehicles (AUVs) and thus causes error between the time and the location of the signal sent compared to the signal received. This error affects the positioning accuracy and real-time positioning of the system. In this study, we constructed a model based on an extended Kalman filter (EKF) in an effort to resolve this problem. The model uses multiple-beacon ranging information, depth information, and velocity information as the primary observations based on which it can manage any amount of beacon distance information. The multi-beacon, EKF-based integrated navigation algorithm yielded results in close agreement with experimental data; the system error was restrained and effectively converged. After data pre-processing, the four beacon navigation systems reaches meter-level accuracy. Compared to the traditional LBL positioning system, the integrated navigation algorithm is more accurate over a wider range. It can be used to provide more stable and accurate position information for any given target in real time, making it very well-suited to AUV integrated navigation systems with multiple beacons.