Editorial for Special Issue: Underwater Acoustics, Communications, and Information Processing

Kim, Kiseon; Shevlyakov, Georgy; Kim, Jea Soo; Soufian, Majeed; Statsenko, L. G.

doi:10.3390/app9224873

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…Major military powers worldwide attach great importance to the research in the field of signal confrontation, with communication signal reconnaissance being a focal point. Detecting crucial communication information and deciphering and disrupting it provide significant information and battlefield advantages for the side that conducts reconnaissance [1][2]. Digital signal modulation recognition technology serves as a critical component in communication signal reconnaissance, acting as the foundation and basis for signal demodulation [3][4].…”

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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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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Acoustic modulation signal recognition based on endpoint detection

Xiuquan,

Zhen,

Yeyin

et al. 2024

Sci Rep

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The modulation recognition technology for acoustic signals holds significant research importance in signal demodulation and communication signal reconnaissance, serving as a crucial component and key aspect. This paper investigates the modulation recognition technology for acoustic signals (< 20 kHz) from the perspectives of signal preprocessing and feature extraction. Firstly, it selects seven modulation signals 2ASK, 4ASK, 2FSK, 4FSK, 2PSK, 4PSK, and OFDM as recognition targets and systematically compares the effectiveness of four different endpoint detection algorithms in modulation signal recognition. To further enhance the performance of the short-time energy entropy ratio algorithm, this study introduces three different noise reduction algorithms for optimization. Finally, to accurately identify and distinguish between 2 and 4FSK signals, this study optimizes the related algorithms of the cyclic spectrum by using the kurtosis coefficient value Kur of the cyclic spectrum parameter matrix when the cyclic frequency α = 0 to differentiate between these two signals. The results show that at SNR of 4 dB, the proposed modulation recognition algorithm can effectively distinguish between these two signals, achieving a recognition accuracy of over 99%.

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On the Design of a High Data Rate Underwater Acoustic Receiver

Samir

Eldiwany

ElMoslimany

et al. 2022

2022 5th International Conference on Communications, Signal Processing, and Their Applications (ICCSPA)

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Editorial for Special Issue: Underwater Acoustics, Communications, and Information Processing

Abstract: Information and communication technologies (ICT) have brought forth various useful tools and services, enabling another Internet-based industrial revolution over the last few decades [...]

Cited by 4 publications

References 17 publications

Modulation Signal Recognition Based on Endpoint Detection and Cepstral Parameters

Modulation Signal Recognition Based on Endpoint Detection and Cepstral Parameters

Acoustic modulation signal recognition based on endpoint detection

On the Design of a High Data Rate Underwater Acoustic Receiver

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