Reconstructing Internal Tides Field Based on Sampling by Autonomous Underwater Vehicles

Abstract

This article explores the challenges and solutions of using autonomous underwater vehicles (AUVs) to observe internal waves in the ocean. Internal waves are highly dynamic and complex, and their rapid spatial and temporal variability makes it difficult to accurately observe them using AUVs due to Doppler smearing and aliasing effects. To overcome these challenges, the article proposes a Sparse Approximation (SA) method that reconstructs the sectional field of internal tides using AUV sampling data on horizontal-reciprocating vertical-dimension sawtooth (i.e., yo-yo) trajectories. The SA method is validated by numerical simulations in a temperature field of internal tides, and a series of reconstructions comparing the SA and linear interpolation method are conducted. The results show that the SA method produces accurate results if the signal frequency of the field is less than the Nyquist frequency of the water column sampling near the horizontal sides of the section. Notably, it reveals that the Doppler smearing and aliasing effects in the SA reconstructions can be effectively suppressed in the vertical isotherm fluctuation frequency range which is less than the Nyquist frequency of the water column sampling near the horizontal sides of the section. Additionally, the article evaluates the robustness of the SA method under different conditions, including AUV sampling behaviors, temperature field variation, AUV sensing and navigation errors. The findings demonstrate that the SA reconstruction method can be effectively used to observe internal tides with AUVs, and provide a design principle for an AUV reciprocating yo-yo sampling trajectory to achieve feasible field reconstruction of internal tides.