An Overview of Atomic Norm Minimization Methods for DOA Estimation in Underwater Acoustics

Underwater acoustics, governing the propagation and scattering of sound in the complex ocean environment characterized by multipath propagation, varied sound speed profile, and high ambient noise, is fundamental to acoustic applications like source localization, active sonar, and ocean acoustic tomography. Direction of Arrival (DOA) estimation is critical in this context, as it provides the direction location of acoustic sources using hydrophone arrays. Atomic norm minimization (ANM) theory has found its way into DOA estimation, alongside well-known compressive sensing-based and subspace-based methods, which enables gridless approaches to DOA estimation. This paper provides an overview of recent work on ANM-based DOA estimation. These new methods are motivated by techniques in atomic norm and the Vandermonde decomposition. Most of them have been proposed for locating the acoustic sources in challenging scenarios that require computational efficiency, high robustness performance, super-resolution capability, and even integration with deep learning techniques. These approaches provide important support for the further development of array signal processing in underwater acoustics.