Motion-aware sonar denoising for autonomous underwater vehicles self-noise using a speed-conditioned...

Passive sonar surveillance by autonomous underwater vehicles (AUVs) is often hindered by non-stationary, nonlinear speed-dependent self-noise. To address this, we propose Speed-UT2-CGAN, a motion-aware sonar denoising framework utilizing a dual-branch conditional generative adversarial network that combines a U-Net convolutional branch for local feature extraction from time-domain audio sequences and a transformer-based attention branch for long-range temporal dependencies. The architecture incorporates AUV speed as an additional conditioning input to dynamically adapt to speed-dependent noise characteristics, and is trained with a combination of adversarial, time-domain, and frequency-domain loss functions to ensure accurate spectral and temporal reconstruction. Experiments on synthetic mixtures combining real AUV self-noise recordings from lake trials with ShipsEar vessel signals demonstrate that Speed-UT2-CGAN significantly outperforms traditional methods, speech enhancement generative adversarial network, and dual-path recurrent neural network, for a single AUV in shallow-water lake trials at 0, 2, and 3 knots, achieving an output average signal-to-noise ratio of 6.6 at -5 dB input and an average correlation coefficient of 0.87. These results confirm the effectiveness of motion-aware speed conditioning for passive sonar enhancement in single-sensor AUV systems, under controlled synthetic-data conditions representative of AUV constant depth, speed, and heading in shallow-water lake environments.