A Novel CCA-NMF Whitening Method for Practical Machine Learning Based Underwater Direction of Arrival Estimation

Abstract

Underwater direction of arrival (DOA) estimation has always been a very challenging theoretical and practical problem. Due to the serious non-stationary, non-linear, and non-Gaussian characteristics, machine learning based DOA estimation methods trained on simulated Gaussian noised array data cannot be directly applied to actual underwater DOA estimation tasks. In order to deal with this problem, environmental data with no target echoes can be employed to analyze the non-Gaussian components. Then, the obtained information about non-Gaussian components can be used to whiten the array data. Based on these considerations, a novel practical sonar array whitening method was proposed. Specifically, based on a weak assumption that the non-Gaussian components in adjacent patches with and without target echoes are almost the same, canonical correlation analysis (CCA) and non-negative matrix factorization (NMF) techniques are employed for whitening the array data. With the whitened array data, machine learning based DOA estimation models trained on simulated Gaussian noised datasets can be used to perform underwater DOA estimation tasks. Experimental results illustrated that, using actual underwater datasets for testing with known machine learning based DOA estimation models, accurate and robust DOA estimation performance can be achieved by using the proposed whitening method in different underwater conditions.

Graphical abstract

Keywords

direction of arrival (DOA) / sonar array data / underwater disturbance / machine learning / canonical correlation analysis (CCA) / non-negative matrix factorization (NMF)