OAE-EEKNN: An Accurate and Efficient Automatic Modulation Recognition Method for Underwater Acoustic Signals

Huang, Zihao; Li, Shuang; Yang, Xiaobo; Wang, Jingjing

doi:10.1109/lsp.2022.3145329

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…One possible interpretation of frequency content fragmentation in frequency-domain acoustic signals is as a filtering product [21]. In order to evaluate and categories acoustic signals, the nervous system receives information from all areas of the auditory pathway, and each area of the pathway interprets a unique component of frequency [22][23][24]. Due to the fact that a spatial frequency transmitter's item is analogous to a time domain convent signal, the speed element might be efficiently achieved by means of time-domain complexity parallel processing.…”

Section: Related Workmentioning

confidence: 99%

Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique

Ashok,

Latha

2024

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Section: Related Workmentioning

confidence: 99%

Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique

Ashok,

Latha

2024

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“…For frequency-domain acoustic signals, the fragmentation of frequency content could be understood as a filtration product [21]. Various locations in the auditory pathway interpret different elements of frequency, and also the nervous system gets information from all regions for the evaluation and classification of acoustic signals [22][23][24]. The item of a spatial frequency transmitter would be equivalent to the convent signal of the time domain, so the speed element might be quickly accomplished through parallel processing of time-domain complexity.…”

Section: Related Workmentioning

confidence: 99%

Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique

Ashok¹,

Latha²

2022

Preprint

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Due to the complex and interdependent world, underwater acoustic source detection is a difficult challenge. It can be extremely difficult to perform Underwater Acoustic Target Recognition (UATR) utilizing ship-radiated noise due to the complicated aquatic organisms. This paper presents a new deep neural network called Audio Perspective based RCNN (APRCNN), learned by initial underwater acoustic waves with Depth Wise Separable (DWS) Convolutional Deep Neural Network. The APRCNN would then be implemented for underwater acoustic signal prediction and classification. The proposed harvester learning features & integration layers of the SPDNN system were motivated by perceptual processing, and the time-dilated convolution was influenced by machine translation. In this article, the researchers provide a methodology for identifying underwater targets using a thick DNN model. The proposed framework was created to reallocate all previous feature maps to maximize the categorization rate while minimizing the calculation burden in various degraded scenarios. Additionally, the proposed technique allows using the actual audio signal in the transfer function as the right network input information rather than using time-frequency spectrum analyzer images. Based on research findings assessed on a real-world collection of passive sonar, our classification method beats other cutting-edge DNN systems & classic ML methods, achieving an accuracy level of 98.4% at 0-dB SNR.

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“…Dai et al [9] carried out wavelet denoising and timefrequency feature extraction for the received signal and used the decision tree model for modulation recognition. Huang et al [10] extracted entropy features and morphological features, and designed optimized autoencoder (OAE) and evaluation-enhanced k-nearest neighbor (EEKNN) algorithms to recognize modulation types.…”

Section: Introductionmentioning

confidence: 99%

Modulation Recognition Method for Underwater Acoustic Communication Signals Based on Passive Time Reversal-Autoencoder with the Synchronous Signals

Bao

Sun

et al. 2023

Sensors

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In the process of the modulation recognition of underwater acoustic communication signals, the multipath effect seriously interferes with the signal characteristics, reducing modulation recognition accuracy. The existing methods passively improve the accuracy from the perspective of selecting appropriate signal features, lacking specialized preprocessing for suppressing multipath effects. So, the accuracy improvement of the designed modulation recognition models is limited, and the adaptability to environmental changes is poor. The method proposed in this paper actively utilizes common synchronous signals in underwater acoustic communication as detection signals to achieve passive time reversal without external signals and designs a passive time reversal-autoencoder to suppress multipath effects, enhance signals’ features, and improve modulation recognition accuracy and environmental adaptability. Firstly, synchronous signals are identified and estimated. Subsequently, a passive time reversal-autoencoder is designed to enhance power spectrum and square spectrum features. Finally, a modulation classification is performed using a convolutional neural network. The model is trained in simulation channels generated by Bellhop and tested in actual channels which are different from the training period. The average recognition accuracy of the six modulated signals is improved by 10% compared to existing passive modulation recognition methods, indicating good environmental adaptability as well.

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OAE-EEKNN: An Accurate and Efficient Automatic Modulation Recognition Method for Underwater Acoustic Signals

Cited by 8 publications

References 18 publications

Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique

Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique

Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique

Modulation Recognition Method for Underwater Acoustic Communication Signals Based on Passive Time Reversal-Autoencoder with the Synchronous Signals

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