Wave-Robust Passive AUV Localization Using FP-MUSIC
Organizations: Software Quality Research Group, IT University of Copenhagen, Denmark
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%.
Figures & tables
| Azimuth RMSE ( ∘ ) | Elevation RMSE ( ∘ ) | |||
|---|---|---|---|---|
| Sea State | MUSIC | FP-MUSIC | MUSIC | FP-MUSIC |
| 1 | 0.61 | 0.25 | 0.57 | 0.02 |
| 2 | 1.21 | 0.39 | 1.17 | 0.31 |
| 3 | 1.81 | 1.12 | 1.96 | 0.87 |
| 4 | 4.43 | 3.32 | 4.20 | 2.37 |
| 5 | 6.95 | 6.88 | 6.48 | 4.43 |
| Quantity | MUSIC | FP-MUSIC |
|---|---|---|
| Azimuth, ( ∘ ) | 0.78 | 0.25 |
| Azimuth, ( ∘ ) | 1.00 | 0.36 |
| Elevation, ( ∘ ) | 1.02 | 0.38 |
| Elevation, ( ∘ ) | 1.04 | 0.37 |
| Range, (m) | 0.07 | 0.07 |
| Range, (m) | 0.10 | 0.10 |