Deep Learning-Based Direction of Arrival Estimation for Underwater Acoustic Sources Using BPSK Signals and Multiple Array Geometries

Accurate direction of arrival (DoA) estimation is critical for underwater acoustic applications such as target localization, communication, and environmental monitoring. The performance of DoA estimation algorithms strongly depends on the geometry of the sensor array used. This paper presents a comprehensive performance comparison of the commonly used array configurations linear and circular arrays for underwater DoA estimation. The study simulates five acoustic sources in a multipath environment and analyzes received signals at two different array geometries with eight hydrophone elements. To enhance source localization, study employs a Convolutional Neural Network (CNN) trained on received time-domain signals. The CNN model achieves an average DoA estimation accuracy of 2.5° RMSE with circular arrays and 4.2° RMSE with linear arrays across all sources. The model was tested under varying signal-tonoise ratios (SNRs) from 0 dB to 20 dB. At 10 dB SNR, the circular array maintained $90 \%$ accuracy (within ± 5°), while the linear array dropped to $76 \%$ accuracy, demonstrating higher robustness of the circular configuration in noisy environments. In terms of angular resolution, the circular array resolved sources separated by as little as 8°, compared to 12° for the linear array. However, the linear array required $25 \%$ less training time and approximately $30 \%$ lower computational cost during inference due to its simpler geometry and reduced spatial diversity.