Modulation Signal Recognition of Underwater Acoustic Communication Based on Archimedes Optimization Algorithm and Random Forest

Wang, Maofa; Zhu, Zhenjing; Qian, Gaofeng

doi:10.3390/s23052764

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…Modern techniques use integrated bathymetric and global navigation satellite system (GNSS) positioning system instruments and these approaches have revolutionized the field of dam BT and profile representation (Fuska et al2014a;Phyoe et al2020). These tools enable precise data collection and visualization of the elevation of the dam's bed floor, facilitating the identification of potential risks and the implementation of appropriate maintenance measures (Zhang and Sun, 2023). The application of these advanced technologies enhances the accuracy and efficiency of dam BT monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…By monitoring the dam bottom mphology regularly, engineers, dam managers, and operators can promptly identify trends, patterns, and potential risks (Wu et al, 2021), enabling timely decision-making regarding maintenance and safety measures. Thus, the hydrographic data collected from dams provide valuable insights for assessing the dam's condition (Zhang and Sun, 2023), aiding in detecting changes such as erosion, sedimentation, or deposition that could compromise the dam's structural stability (Wang et al, 2020). Analysis and results from these data empower stakeholders to make well-informed decisions to ensure the dam's safety and longevity (Chen et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Study of Short-term Longitudinal Profile and Sediment Dynamics of Ikpoba Reservoir

Alademomi,

Oladosu,

Matthew

2024

Preprint

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Complex depositional systems, exemplified by dam reservoirs, pose significant challenges to natural sediment flow in river courses, impacting operational efficiency. This study focuses on the Ikpoba Dam, a critical water source for Benin City, facing functionality issues due to sedimentation. Through repeated bathymetric surveys conducted during wet and dry seasons, the research maps the dam's bed-floor topography. Analysis using empirical formulae and a geospatial approach unveils short-term sediment accumulation dynamics, emphasizing seasonal variations. Results indicate a sediment volume of 400,000 cubic meters in the 2019 survey, translating to 73.33% loss in storage capacity over 44 years, at an estimated rate of 1.66 m^3/year. Verification of bed profile results demonstrates high reliability, with root mean square errors ranging from 90–93%, employing ArcGIS 10.4. Notably, the study identifies a substantial sediment accumulation of 840,127.34 cubic meters. These findings are pivotal for formulating dredging plans essential for dam revamping initiatives, aiming to optimize efficiency while mitigating sedimentation challenges.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Study of Short-term Longitudinal Profile and Sediment Dynamics of Ikpoba Reservoir

Alademomi,

Oladosu,

Matthew

2024

Preprint

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“…Fang [15] and colleagues employed a recognition method based on Random Forests to automatically identify four underwater acoustic signal modulation methods: OFDM, 2FSK, 4FSK, and 8FSK. Wang [16] and colleagues used Random Forests as classifiers, achieving a modulation method recognition accuracy of over 95% in underwater acoustic signals when the SNR ratio was above -5dB.…”

Section: Introductionmentioning

confidence: 99%

Modulation Signal Recognition Based on Endpoint Detection and Cepstral Parameters

Xiuquan,

Zhen,

Yeyin

et al. 2024

Preprint

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Digital signal modulation recognition technology serves as the foundation and basis for signal demodulation, playing a crucial role in communication signal reconnaissance and holding significant research significance. This paper conducts research on digital signal modulation recognition technology from the perspectives of signal preprocessing and feature extraction. Seven modulation signals, namely 2ASK, 4ASK, 2FSK, 4FSK, 2PSK, 4PSK, and OFDM, are selected as recognition targets. The paper compares the effects of four different endpoint detection algorithms on modulation signal recognition. The results indicate that, for these seven modulation signals, the short-time energy entropy ratio algorithm performs the best, achieving a correct endpoint detection rate of over 93% in a Gaussian channel with a signal-to-noise(SNR) ratio of 0dB. Based on this, three different denoising algorithms are introduced to further enhance the performance of the short-time energy entropy ratio algorithm. The results show that the wavelet denoising algorithm achieves the greatest improvement in the performance of the short-time energy entropy ratio algorithm, with a short processing time. In a Gaussian channel with a SNR ratio greater than − 10dB, the endpoint detection accuracy of this algorithm can be maintained at over 95%. Finally, for the accurate identification and differentiation of 2FSK and 4FSK, this paper optimized the relevant algorithm in the cyclic spectrum. The kurtosis coefficient value Kur of the cyclic spectrum parameter matrix at the cyclic frequency \(\alpha =0\) is utilized to distinguish between these two signals. The results show that, at a SNR ratio of 4dB, the modulation recognition algorithm proposed in this paper can effectively distinguish between these two signals, achieving a recognition accuracy of over 99%.

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Advances and applications of machine learning in underwater acoustics

Niu,

Li,

Zhang

et al. 2023

Intell. Mar. Technol. Syst.

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Recent advancements in machine learning (ML) techniques applied to underwater acoustics have significantly impacted various aspects of this field, such as source localization, target recognition, communication, and geoacoustic inversion. This review provides a comprehensive summary and evaluation of these developments. As a data-driven approach, ML played a pivotal role in discerning intricate relationships between input features and desired labels based on the provided training dataset. They are achieving success in ocean acoustic applications through ML hinges on several critical factors, including well-designed input feature preprocessing, appropriate labels, choice of ML models, effective training strategy, and availability of ample training and validation datasets. This review highlights noteworthy results from published studies to illustrate the effectiveness of ML methods in diverse application scenarios. In addition, it delves into the essential techniques employed within these applications. To understand the utility of ML in underwater acoustics, one must analyze its advantages and limitations. This assessment will aid in identifying scenarios where ML excels and those where it may face challenges. In addition, it provides insights into promising avenues for future research, shedding light on potential research directions that warrant exploration.

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Modulation Signal Recognition of Underwater Acoustic Communication Based on Archimedes Optimization Algorithm and Random Forest

Cited by 6 publications

References 33 publications

Study of Short-term Longitudinal Profile and Sediment Dynamics of Ikpoba Reservoir

Study of Short-term Longitudinal Profile and Sediment Dynamics of Ikpoba Reservoir

Modulation Signal Recognition Based on Endpoint Detection and Cepstral Parameters

Advances and applications of machine learning in underwater acoustics

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