Deep learning-based time delay estimation for motion compensation in synthetic aperture sonars

Abstract

Accurate and robust time delay estimation is crucial for synthetic aperture sonar (SAS) imaging. A two-step time delay estimation method based on displaced phase centre antenna (DPCA) micronavigation has been widely applied in motion estimation and compensation of SASs. However, the existing methods for time delay estimation are not sufficiently robust, which reduces the performance of SAS motion estimation. Deep learning is currently one of the cutting-edge techniques and has brought about a remarkable progress in the field of underwater acoustic signal processing. In this study, a deep learning-based time delay estimation method is introduced to SAS motion estimation and compensation. The subband processing is first applied to obtain ambiguous time delays between adjacent pings from phases of SAS echoes. Then, a lightweight neural network is utilised to construct phase unwrapping. The model of the employed neural network is trained with simulation data and applied to real SAS data. The results of time delay estimation and motion compensation demonstrate that the proposed neural network-based method has much better performance than the two-step and joint-subband methods.