Underwater Acoustic Research Trends with Machine Learning: General Background

Abstract

Underwater acoustics is the study of all phenomena related to the occurrence, propagation, and reception of sound waves in the water medium. Because electromagnetic waves undergo a significant attenuation in water, sound waves, which have relatively low propagation loss and high propagation speed, are used for underwater communication and detection. In the field of underwater acoustics, studies are mainly conducted on underwater communications, underwater target detection, marine resources, and measurement and analysis of underwater sound sources. Most applications for underwater acoustics can be described as remote sensing. Remote sensing is employed when an object, condition, or phenomenon of interest cannot be directly observed and information about the target of interest is acquired indirectly using data. In underwater acoustics, this can be described simply as a sound navigation and ranging (sonar) system. Sonar systems can be broadly classified into passive and active systems. Passive sonar systems acquire information by using sensors to measure the acoustic energy (signal) emitted by the target of interest. In active sonar systems, the observer obtains information by directly emitting an acoustic pulse and gathering the returning signals that are reflected by the target. Machine learning, which is widely known today, was initially used in academia for developing artificial intelligence. Recently, the use of machine learning has become widespread owing to the introduction of high-speed parallel computing that uses graphics processing units (GPUs) and can perform reliable learning based on big data, as well as develop various machine learning techniques that can find optimal solutions. Machine learning has contributed to the evolution of acoustic signal processing and voice recognition, and it is also utilized in various ways in the field of underwater acoustics. It is used for traditional remote sensing, such as in detection/classification of underwater sound sources and targets and localization. In addition, it is being used in the field of acoustic signal processing for seabed classification and marine environment information extraction and is producing an abundance of scientific results. Data-driven machine learning divides the data into a training set and test set. The training set is used to create a model that is suitable for machine learning, and the model's accuracy is increased through a repetitive model update process in which the model is validated via the

A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition

“…An autoencoder, a kind of unsupervised neural structure, decreases dimensions or finds feature vectors [1]. Autoencoders are widely adopted algorithms that are programmed to reproduce their input to output.…”

“…Underwater acoustics, the mutual domain for the analysis of all the processes associated with the generation, occurrence, transmission, and reception of sound pulses in the water medium and its interference with boundaries, has mostly been implemented to submarine communication, the examination of maritime assets and environment surveying, target and object recognition, and measurement and study of acoustic sources in underwater atmosphere. Remote sensing, which can be described as a sonar system, is applied when a target of interest cannot be directly verified, and the information about it is to be gained secondarily [1]. Sonar, a collective term for numerous devices that use sound waves as the information carrier, is a technique that permits ships and other vessels to discover and recognize substances in the water through a system of sound rhythms and echoes [2].…”

Peak to average power ratio alleviation by utilizing swarm intelligence along with machine learning for multiple input multiple output‐orthogonal frequency division multiplexing based underwater acoustic communication system

“…Beyond tool for cognitive operation in particular image recognition, machine learning has also exhibits its capability and usage in communication systems by providing improvement over classic techniques 18,19 . On that account, machine learning has contributed to the evolution of acoustic signal processing and voice recognition, and it is also utilized in various ways in the field of underwater acoustics 20 . Furthermore, it is being used in the field of acoustic signal processing for detection/classification of underwater sound sources, seabed classification, and marine environment information extraction and are yielding an effective scientific outcomes.…”

Assessment of Arrow-of-Time Metrics for the Characterization of Underwater Explosions

“…All of these activities produce characteristic acoustic events that can be automatically detected [ 7 , 8 ] and employed to devise law enforcement tools and control mechanisms. It would be desirable to develop UNDEX detection procedures that do not just rely on the reported data.…”