Underwater Acoustic Source Localization via Kernel Extreme Learning Machine

Abstract

Fiber-optic hydrophones have received extensive research interests due to their advantage in ocean underwater target detection. Here, kernel extreme learning machine (K-ELM) is introduced to source localization in underwater ocean waveguide. As a data-driven machine learning method, K-ELM does not need a priori environment information compared to the conventional method of match field processing. The acoustic source localization is considered as a supervised classification problem, and the normalized sample covariance matrix formed over a number of snapshots is utilized as an input. The K-ELM is trained to classify sample covariance matrices (SCMs) into different depth and range classes with simulation. The source position can be estimated directly from the normalized SCMs with K-ELM. The results show that the K-ELM method achieves satisfactory high accuracy on both range and depth localization. The proposed K-ELM method provides an alternative approach for ocean underwater source localization, especially in the case with less a priori environment information.