Underwater Target Detection Based on Machine Learning

Zhang, Wen; Wu, Yanqun; Lin, Yonggang; Ma, Lei; Han, Kyungsik; Chen, Yu; Liu, Chen

doi:10.1109/icicsp50920.2020.9232081

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…M denotes the number of characteristic attributes. Then, the Gini coefficient of attribute h can be expressed as Equation (19).…”

Section: The Dt Algorithmmentioning

confidence: 99%

“…In Equation (19), D m denotes the set of the samples characterized by the attribute h m . The optimal segmentation attributes h * are determined as follows:…”

Section: The Dt Algorithmmentioning

confidence: 99%

“…Recently, ML integrated with underwater acoustics has gained significant attention, which is a type of optimization method from the statistical perspective [18][19][20][21]. The decision tree (DT) technique is commonly used and efficient for classification in ML, depending on feature characteristics for tree construction [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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Accurate Identification for CW Direct Signal in Underwater Acoustic Ranging

Li,

Fu,

Zou

2024

JMSE

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The underwater channel is bilateral, heterogeneous, uncertain, and exhibits multipath transmission, sound line curvature, etc. These properties complicate the structure of the received pulse, causing great challenges in direct signal identification for ranging purposes and impacts on back-end data processing, even accurate acoustic positioning. Machine learning (ML) combined with underwater acoustics has emerged as a prominent area of research in recent years. From a statistical perspective, ML can be viewed as an optimization strategy. Nevertheless, the existing ML-based direct-signal discrimination approaches rely on independent assessment, utilizing a single sensor (beacon or buoy), which is still insufficient for adapting to the complex underwater environment. Thus, discrimination accuracy decreases. To address the above issues, an accurate CW direct signal detection approach is performed using the decision tree algorithm, which belongs to ML. Initially, the pulse parameter characteristics in the underwater multipath channel are investigated and the parameter models are built. Then, based on multi-sensor localization performance feedback, fusion characteristics for diverse pulse are created. Next, the pulse parameter characteristics are preprocessed to mitigate the impact of varying magnitudes and units of magnitude on data processing. Then, the decision tree is built to obtain the desired output results and realize accurate recognition of the ranging direct signals. Finally, the feasibility and reliability of this paper’s method are verified by computer simulation and field testing.

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“…M denotes the number of characteristic attributes. Then, the Gini coefficient of attribute h can be expressed as Equation (19).…”

Section: The Dt Algorithmmentioning

confidence: 99%

“…In Equation (19), D m denotes the set of the samples characterized by the attribute h m . The optimal segmentation attributes h * are determined as follows:…”

Section: The Dt Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Accurate Identification for CW Direct Signal in Underwater Acoustic Ranging

Li,

Fu,

Zou

2024

JMSE

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“… Traditional detection and recognition methods of ocean acoustic observation signals need to extract target features manually. With the development of deep learning theory, deep learning which has been widely used in computer vision, signal processing, and other fields [94]- [100] is also introduced into this field. In terms of higher-order cumulants, the feasible performance of using higher-order spectral features to implement the ocean noise classifier based on an artificial neural network [45] indicates that the application of higher-order domain feature extraction technology in the field of ocean acoustic observations is about to evolve from traditional methods such as vector machines to neural networks.…”

Section: Future Trendsmentioning

confidence: 99%

High-Order Domain Feature Extraction Technology for Ocean Acoustic Observation Signals: A Review

et al. 2023

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For the acquisition of ocean observation information, the underwater acoustic signal is the only known medium that can propagate over long distances in water. However, the underwater acoustic field environment is highly time-varying and spatially variable, and ocean acoustic observation signals are mixed Gaussian, non-cooperative, and nonlinear. Therefore, feature extraction of underwater target signals has been a challenging research direction. The high-order domain feature extraction method of ocean acoustic observation signal is widely used because of its advantages of anti-Gaussian background noise and preservation of signal phase information. Firstly, this paper analyzes three aspects, including the characteristics of ocean acoustic observation signals, the limitations of second-order domain processing methods, and advantages of high-order domain processing methods. Then, this paper focuses on theoretical advantages and application areas of signal high-order domain feature extraction technology. The technical development and application performance of common feature extraction technology in the field of ocean acoustic observation signal processing by higher-order domain methods in recent years are analyzed in the paper, and the challenges and future trends of higher-order domain feature extraction technology in marine acoustic observation signal processing are discussed in the context of recent studies.

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