An integrated tracking technique for underwater navigation using acoustic and IMU measurements

Accurate navigation is essential for underwater vehicles like AUVs, which often operate in deep or remote areas. However, complex ocean dynamics, cumulative inertial-measurement unit (IMU) drift, and diverse noise sources often result in erratic and unreliable position estimates. To overcome these challenges, we proposed a method that combines underwater acoustic signals with onboard motion sensor data to improve the underwater position tracking system. The proposed system uses a long baseline (LBL) acoustic array of surface buoys to capture the Time-difference-of-Arrival (TDoA) of a multi-pulse beacon. We extract arrival times using a superimposed-envelope-spectrum (SES) detector, which exploits the beacon’s periodic structure to stay reliable even in heavy noise. These acoustic measurements are fused with six-degree-of-freedom IMU data using a particle filter (PF). The filter suppresses IMU drift and reveals how long dead-reckoning remains reliable before an acoustic update becomes essential. Simulation results demonstrated that our PF-TDoA fusion method achieved up to 40% reduction in mean localization error compared to traditional fusion filters and optimization method. In the experiment, we compared the simulated IMU prediction with real-world acoustic measurements, and the resulting fused position estimated remained within 3 m of Global Positioning System (GPS)-reported trajectory, demonstrating robust performance under operational conditions. • We introduce a feature-based TDoA detector that reduces false alarms in heavy noise. • We fuse 6-DOF IMU and acoustic ranges in a PF to schedule pings only when needed. • We present a geometry-aware robust NLLS solver for accurate 3-D trilateration.