Snapping shrimp noise mitigation based on statistical detection in underwater acoustic orthogonal frequency division multiplexing systems

Kim, Hyeonsu; Seo, Jongpil; Ahn, Jongmin; Chung, Jaehak

doi:10.7567/jjap.56.07jg02

Cited by 10 publications

(5 citation statements)

References 26 publications

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“…generated by propellers, hydraulic pumps, snapping shrimp etc. [80] [81]. In order to provide a generic noise model, not specific to a particular location in the ocean, we can approximate the common sources of noise using Gaussian statistics and a continuous PSD as described by Stojanovic and Preisig [16] [82].…”

Section: E Ambient Noise Powermentioning

confidence: 99%

Channel Modeling for Underwater Acoustic Network Simulation

et al. 2020

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Simulation forms an important part of the development and empirical evaluation of underwater acoustic network (UAN) protocols. The key feature of a credible network simulation model is a realistic channel model. A common approach to simulating realistic underwater acoustic (UWA) channels is by using specialised beam tracing software such as BELLHOP. However, BELLHOP and similar modeling software typically require knowledge of ocean acoustics and a substantial programming effort from UAN protocol designers to integrate it into their research. This paper is a distilled tutorial on UWA channel modeling with a focus on network simulation, providing a trade-off between the flexibility of low level channel modeling via beam tracing and the convenience of automated channel modeling, e.g. via the World Ocean Simulation System (WOSS). The tutorial is accompanied by our MATLAB simulation code that interfaces with BELLHOP to produce channel data for UAN simulations. As part of the tutorial, we describe two methods of incorporating such channel data into network simulations, including a case study for each of them: 1) directly importing the data as a look-up table, 2) using the data to create a statistical channel model. The primary aim of this paper is to provide a useful learning resource and modeling tool for UAN protocol researchers. Initial insights into the UAN protocol design and performance provided by the statistical channel modeling approach presented in this paper demonstrate its potential as a powerful modeling tool for future UAN research.

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Section: E Ambient Noise Powermentioning

confidence: 99%

Channel Modeling for Underwater Acoustic Network Simulation

et al. 2020

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“…generated by propellers, hydraulic pumps, snapping shrimp etc. [79] [80]. In order to provide a generic noise model, not specific to a particular location in the ocean, we can approximate the common sources of noise using Gaussian statistics and a continuous PSD as described by Stojanovic and Preisig [16] [81].…”

Section: E Calculating Ambient Noise Powermentioning

confidence: 99%

Channel Modeling for Underwater Acoustic Network Simulation

Morozs¹,

Gorma²,

Henson³

et al. 2020

Preprint

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This manuscript was submitted to IEEE Access on 12 Jun 2020.<div><br></div><div>Abstract:</div><div><br></div><div>Simulation forms an important part of the development and empirical evaluation of underwater acoustic network (UAN) protocols. The key feature of a credible network simulation model is a realistic channel model. A common approach to simulating realistic underwater acoustic (UWA) channels is by using specialised beam tracing software such as BELLHOP. However, BELLHOP and similar modeling software typically require knowledge of ocean acoustics and a substantial programming effort from UAN protocol designers to integrate it into their research. In this paper, we bridge the gap between low level channel modeling via beam tracing and automated channel modeling, e.g. via the World Ocean Simulation System (WOSS), by providing a distilled UWA channel modeling tutorial from the network protocol design point of view. The tutorial is accompanied by our MATLAB simulation code that interfaces with BELLHOP to produce channel data for UAN simulations. As part of the tutorial, we describe two methods of incorporating such channel data into network simulations, including a case study for each of them: 1) directly importing the data as a look-up table, 2) using the data to create a statistical channel model. The primary aim of this paper is to provide a useful learning resource and modeling tool for UAN protocol researchers. Initial insights into the UAN protocol design and performance provided by the statistical channel modeling approach presented in this paper demonstrate its potential as a powerful modeling tool for future UAN research.<br></div>

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“…Due to the above problems, research has been conducted continuously to detect SSN intervals and prevent performance degradation [15][16][17][18][19]. However, SSs are sensitive to the temperature of water, so it has the property of being affected by sunlight.…”

Section: Introductionmentioning

confidence: 99%

Snapping Shrimp Noise Detection Based on Statistical Model

Park,

Seok,

Hong

2023

JMSE

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Snapping Shrimps (SSs) live in a warm ocean except the North and South Poles, and they are characterized by generating strong shock waves underwater using large claws. Shock waves generated by these SSs are used for marine noise research as a signal and as a noise source, because they cause a decrease in the Signal-to-Noise Ratio (SNR), acting as one of the disruptors in fields such as sonar for target detection and underwater communication. A state-of-the-art technique to detect Snapping Shrimp Noise (SSN) is Linear Prediction (LP) analysis. Using the feature where SSN occurs for a very short time, the SSN interval was detected based on the phenomenon where the residuals appear large in the SSN interval when the LP analysis is used. In this paper, we propose an SSN interval detection technique using the Likelihood Ratio (LR) as a follow-up study to the LP-analysis-based method for further performance improvements. The proposed method was used to analyze the statistical distribution characteristics of the LP residual of SSNs compared to Gaussian, Laplace, and Gamma distributions through the Goodness-Of-Fit test. Based on this, the statistical-model-based LRs of the three distributions were computed to detect the SSN interval. Comparing the proposed method with the state-of-the-art method, the proposed method achieved 0.0620, 0.0675, and 0.0662 improvements in Gaussian, Laplace, and Gamma distributions in the Receiver Operating Characteristic curve and Area Under Curve, respectively. The study results confirmed that the proposed method can operate effectively in the marine acoustic environment. This can help find accurate intervals for the automatic labeling of or reduction in SSN.

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Snapping shrimp noise mitigation based on statistical detection in underwater acoustic orthogonal frequency division multiplexing systems

Cited by 10 publications

References 26 publications

Channel Modeling for Underwater Acoustic Network Simulation

Channel Modeling for Underwater Acoustic Network Simulation

Channel Modeling for Underwater Acoustic Network Simulation

Snapping Shrimp Noise Detection Based on Statistical Model

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