Underwater Acoustic Source Localization via Kernel Extreme Learning Machine

Zhengliang Hu,Pan Xu,Mingxing Nan,Guangming Li,Jinxing Huang,Kang Lou

doi:10.3389/fphy.2021.653875

Zhengliang Hu, Pan Xu + Show 4 more

Open Access

https://doi.org/10.3389/fphy.2021.653875

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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.

Highlights

Underwater source localization in ocean waveguides is a vital task in the military and civilian fields, and has become a research focus in applied ocean acoustics [1]
Sound pressure signals received from fiber-optic hydrophone with different frequencies and signal-to-noise ratio (SNR) are utilized to investigate the performance of kernel extreme learning machine (K-ELM) for source localization
The acoustic pressure signal measured by VLA is transformed to frequency domain and preprocessed into normalized sample covariance matrices (SCMs) as the input of the K-ELM

Summary

Introduction

Underwater source localization in ocean waveguides is a vital task in the military and civilian fields, and has become a research focus in applied ocean acoustics [1]. Matrix written as follows [23]: 1) Simulate the acoustic pressure signal with respect to different ranges and depths. 5) Train two K-ELM models for range and depth prediction on the training set.

Results

Conclusion