Conditional Diffusion Generation of DVL and Inertial Signals for Underwater Navigation
Read the original on arXiv Machine Learning →The paper introduces a conditional diffusion framework that generates synthetic Doppler velocity log (DVL) and inertial sensor signals for autonomous underwater vehicle (AUV) navigation. Using an elucidated diffusion model conditioned on three per‑channel motion descriptors, the authors evaluate three configurations—DVL beams alone, inertial channels alone, and all channels jointly—across thirteen real trajectories, achieving Pearson correlations between 0.72 and 0.997. A BeamsNet velocity estimator trained solely on these generated signals matches the performance of one trained on the full real dataset, indicating that synthetic data can replace real recordings without measurable loss.
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