A Steganographic Approach to Sonar Tracking

Park, J. Daniel; Doherty, J.F.

doi:10.1109/joe.2018.2847160

Cited by 4 publications

(1 citation statement)

References 35 publications

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“…It also acts as a major performance degradation factor by causing a decrease in the Signal-to-Noise Ratio (SNR) and it acts as a noise source within the ambient sound level in coastal seas [7,8]. Examples include areas such as human underwater communication [9,10], animal underwater communication [11], sonar signal processing [12,13], and target detection [14]. In particular, in the field of underwater communication, the bit error rate performance of orthogonal frequency division multiplexing systems may be severely degraded due to the influence of frequent SSN [10].…”

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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