# ACGAN-Based Multi-Target Elevation Estimation with Vector Sensor Arrays in Low-SNR Environments

## TL;DR

This paper introduces a new ACGAN-based method for accurate direction-of-arrival estimation in low-SNR environments using vector sensor arrays.

## Contribution

The novel integration of SE attention and MDFA modules in an ACGAN framework improves multi-target elevation estimation in low-SNR conditions.

## Key findings

- The proposed ACGAN architecture improves DOA estimation accuracy in low-SNR environments.
- The MDFA module enhances weak target representation in beamforming maps.
- The auxiliary classifier branch improves multi-source classification and separation performance.

## Abstract

To mitigate the reduced accuracy of direction-of-arrival (DOA) estimation in scenarios with low signal-to-noise ratios (SNR) and multiple interfering sources, this paper proposes an Auxiliary Classifier Generative Adversarial Network (ACGAN) architecture that integrates a Squeeze-and-Excitation (SE) attention mechanism and a Multi-scale Dilated Feature Aggregation (MDFA) module. In this neural network, a vector hydrophone array is employed as the receiving unit, capable of simultaneously sensing particle velocity signals in three directions (vx,vy,vz) and acoustic pressure p, thereby providing high directional sensitivity and maintaining robust classification performance under low-SNR conditions. The MDFA module extracts features from multiple receptive fields, effectively capturing cross-scale patterns and enhancing the representation of weak targets in beamforming maps. This helps mitigate estimation bias caused by mutual interference among multiple targets in low-SNR environments. Furthermore, an auxiliary classification branch is incorporated into the discriminator to jointly optimize generation and classification tasks, enabling the model to more effectively identify and separate multiple types of labeled sources. Experimental results indicate that the proposed network is effective and shows improved performance across diverse scenarios.