Underwater Acoustic Target Recognition Based on Supervised Feature-Separation Algorithm

Ke, Xiaoquan; Yuan, Fei; Cheng, En

doi:10.3390/s18124318

Cited by 54 publications

(23 citation statements)

References 19 publications

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“…As such, it is a much less clear problem than supervised learning, and there is no clear error metric. In underwater acoustics, a considerable number of previous sonar application studies have used machine learning for classification purposes, such as target type/state classification (Choi et al, 2019;Fischell and Schmidt, 2015;Ke et al, 2018;Wang et al, 2019) and target and clutter signal classification (Allen et al, 2011;Murphy and Hines, 2014;Young and Hines, 2007). In many of these studies, the properties of the data that were used for learning were recognized beforehand owing to the goals of the studies.…”

Section: Definitions Types and Basic Concepts Of Machine Learningmentioning

confidence: 99%

Underwater Acoustic Research Trends with Machine Learning: General Background

Yang

Lee

Choo

et al. 2020

J. Ocean Eng. Technol.

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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

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Section: Definitions Types and Basic Concepts Of Machine Learningmentioning

confidence: 99%

Underwater Acoustic Research Trends with Machine Learning: General Background

Yang

Lee

Choo

et al. 2020

J. Ocean Eng. Technol.

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“…For instance, in [15], Cao et al utilized Stacked Autoencoder (SAE) to extract high-level features for ship recognition with short time frequency transform, which provided competitive performance. Ke et al in [16] utilized deep Autoencoder and SVM to classify two classes of underwater acoustic targets. In [17,18], deep belief networks (DBNs) were utilized to the recognition of ship-radiated noise.…”

Section: Related Workmentioning

confidence: 99%

Joint Representation and Recognition for Ship-Radiated Noise Based on Multimodal Deep Learning

Yuan

Cheng

2019

JMSE

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Ship recognition based on ship-radiated noise is one of the most important and challenging subjects in underwater acoustic signal processing. The recognition methods for ship-radiated noise recognition include traditional methods and deep learning (DL) methods. Developing from the DL methods and inspired by audio–video speech recognition (AVSR), the paper further introduces multimodal deep learning (multimodal-DL) methods for the recognition of ship-radiated noise. In this paper, ship-radiated noise (acoustics modality) and visual observation of the ships (visual modality) are two different modalities that the multimodal-DL methods model on. The paper specially designs a multimodal-DL framework, the multimodal convolutional neural networks (multimodal-CNNs) for the recognition of ship-radiated noise. Then the paper proposes a strategy based on canonical correlation analysis (CCA-based strategy) to build a joint representation and recognition on the two different single-modality (acoustics modality and visual modality). The multimodal-CNNs and the CCA-based strategy are tested on real ship-radiated noise data recorded. Experimental results show that, using the CCA-based strategy, strong-discriminative information can be built from weak-discriminative information provided from a single-modality. Experimental results also show that as long as any one of the single-modalities can provide information for the recognition, the multimodal-DL methods can have a much better multiclass recognition performance than the DL methods. The paper also discusses the advantages and superiorities of the multimodal-Dl methods over the traditional methods for ship-radiated noise recognition.

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“…Meanwhile, passive SONAR data without classification are relatively abundant. Yang et al (2018) and Ke et al (2018) have conducted studies utilizing unclassified SONAR data for pre-training to increase the efficiency of supervised learning. Yang et al (2018) used the values of the final hidden layer of the competitive deep-belief network (CDBF) (designed by them) as classifier inputs.…”

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confidence: 99%

“…The competitive layer is placed at the rear of the hidden layer, and training is performed to Fig. 2 The process of underwater acoustic target recognition (Ke et al, 2018). increase the distinction between the clustered classes.…”

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confidence: 99%

“…This method has delivered a classification performance that is superior to those of methods using the Mel-frequency cepstral coefficient (MFCC), waveform, wavelet, or feature vectors based on auditory model. Ke et al (2018) also used unclassified passive data to improve the accuracy of learning performed with marginal amounts of classified passive SONAR data. In their study, the following four steps were used for classifying ships: (1) pre-processing, (2) pre-training, 3fine-tuning, and (4) classification (Fig.…”

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Underwater Acoustic Research Trends with Machine Learning: Passive SONAR Applications

Yang

Lee

Choo

et al. 2020

J. Ocean Eng. Technol.

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Underwater acoustics is a scientific domain that involves the study of the phenomena of sound waves in water, including their generation, propagation, and reception. Specifically, the sound navigation and ranging (SONAR) system is utilized to investigate underwater communication and target detection and to study marine resources and the environment; further, it is utilized to measure and analyze sound sources in water. The main objective of underwater acoustics-based remote sensing is the indirect acquisition of information on underwater targets of interest using acoustic data. At present, highly advanced data-driven machine-learning techniques are being applied in various ways for extracting information from acoustic data. The techniques closely related to these applications are introduced in the first part of this paper (Yang et al., 2020). This paper presents a detailed review of the applications of machine learning in underwater acoustics and passive SONAR signal processing. 2. Passive SONAR Signal Processing 2.1 Passive Target Detection and Identification Signals measured by a passive SONAR system exhibit fluctuations owing to irregular noises in the ocean. This hinders target signal detection. The conventional signal processing method for detecting target signals is based on the Neyman-Pearson criterion (Nielsen, 1991). As the probability distribution of the received signals, including the target signals, differs from that of the noise signals, the probability ratio that is set according to the presence of the target signal at the time of observation is compared with a preset value. This helps determine whether the target signal is included in the observed time period. This technique can be expanded to detect the target signal by comprehensively analyzing all the signals measured in the time domain of interest as well as signals observed at a specific time. In general, techniques for detecting a target signal through comparison with a threshold value have a disadvantage: false alarms can occur frequently, particularly in the scenario of a low signal

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Underwater Acoustic Target Recognition Based on Supervised Feature-Separation Algorithm

Cited by 54 publications

References 19 publications

Underwater Acoustic Research Trends with Machine Learning: General Background

Underwater Acoustic Research Trends with Machine Learning: General Background

Joint Representation and Recognition for Ship-Radiated Noise Based on Multimodal Deep Learning

Underwater Acoustic Research Trends with Machine Learning: Passive SONAR Applications

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