# Research on AUV Underwater Localization Method Based on an n-Shaped Array

## TL;DR

This paper introduces a new method for localizing an autonomous underwater vehicle (AUV) using an n-shaped hydrophone array to improve recovery accuracy.

## Contribution

A novel AUV localization method using an n-shaped array with MUSIC and SAGE algorithms for improved underwater positioning.

## Key findings

- The proposed method effectively handles coherent signals caused by underwater transmission impairments.
- Simulation results show the method achieves good parameter estimation performance.
- The algorithm is extended to support both far-field and near-field localization scenarios.

## Abstract

During continuous navigation of the mother ship, an autonomous underwater vehicle (AUV) can be recovered through an underwater hangar, and the accurate localization of the AUV relative to the mother ship is a key step in the recovery process. To address the AUV localization problem, an n-shaped hydrophone array is designed based on the spatial configuration of the underwater hangar. Since underwater acoustic signals are susceptible to multipath propagation, co-channel interference, and other transmission impairments, the signals received by the array often exhibit coherence. Accordingly, a far-field sound source localization method based on the n-shaped array is proposed. The proposed algorithm first applies spatial smoothing to the x-axis and y-axis subarrays and jointly constructs a received data vector, followed by eigenvalue decomposition of the corresponding covariance matrix. The Multiple Signal Classification (MUSIC) algorithm is then employed to obtain coarse estimates of the source angles. These coarse estimates are subsequently used as initial values for the Space-Alternating Generalized Expectation-maximization (SAGE) algorithm, which performs refined optimization of the angular parameters in a continuous parameter space, thereby effectively improving estimation accuracy. Furthermore, the proposed algorithm is extended from far-field scenarios to near-field localization. Simulation results demonstrate that the proposed method achieves good parameter estimation performance.