Wave-Robust Passive AUV Localization Using FP-MUSIC
cs.RO
Submitted: 2026-09-23
Updated: 2026-09-23
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
The gist: Localizing an autonomous underwater vehicle without pre-deployed seabed transponders, or direct access to onboard vehicle sensors remains a core challenge.
Terminology
Abstract
Localizing an autonomous underwater vehicle without pre-deployed seabed transponders, or direct access to onboard vehicle sensors remains a core challenge. We present a receiver-passive 3-D localization and spatial mapping system utilizing a single floating surface buoy equipped with a hydrophone array and an inertial measurement unit (IMU). The central difficulty is that surface wave motion induces six-degree-of-freedom (6-DOF) perturbations that rotate the array between snapshots, degrading conventional subspace processing. We resolve this by introducing a fixed-point iterative MUltiple SIgnal Classification algorithm (FP-MUSIC) that uses IMU measurements to de-warp snapshot covariances prior to direction-of-arrival estimation. Furthermore, we employ a subspace-projected wideband matched filter to resolve beacon ranges and use power asymmetry for independent front-back identification. Evaluations across simulated sea states demonstrate that FP-MUSIC substantially reduces localization error relative to uncompensated methods and sustains robust 3-D tracking and vehicle orientation estimation under wave-induced motion. At moderate sea state, FP-MUSIC increases the 2-m beacon-separation accuracy from approximately 45% to 75%.
Sources
- Raspi$^2$USBL: An open-source Raspberry Pi-Based Passive Inverted Ultra-Short Baseline Positioning System for Underwater Robotics
- Direction of Arrival Estimation Using Microphone Array Processing for Moving Humanoid Robots
- A Bayesian Compressed Sensing Kalman Filter for Direction of Arrival Estimation
- Subspace-based compressive sensing algorithm for raypath separation in a shallow-water waveguide
- Machine Learning-Based Estimation Of Wave Direction For Unmanned Surface Vehicles
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