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基于自动编码器的声呐浮标信号收发的信号调制与解调方法

Autoencoder-Based Signal Modulation and Demodulation Methods for Sonobuoy Signal Transmission and Reception.

作者信息

Park Jinuk, Seok Jongwon, Hong Jungpyo

机构信息

School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea.

Department of Information and Communication Engineering, Changwon National University, Changwon 51140, Korea.

出版信息

Sensors (Basel). 2022 Aug 29;22(17):6510. doi: 10.3390/s22176510.

DOI:10.3390/s22176510
PMID:36080975
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9460657/
Abstract

Sonobuoy is a disposable device that collects underwater acoustic information and is designed to transmit signals collected in a particular area to nearby aircraft or ships and sink to the seabed upon completion of its mission. In a conventional sonobuoy signal transmission and reception system, collected signals are modulated and transmitted using techniques such as frequency division modulation or Gaussian frequency shift keying. They are received and demodulated by an aircraft or a ship. However, this method has the disadvantage of a large amount of information being transmitted and low security due to relatively simple modulation and demodulation methods. Therefore, in this paper, we propose a method that uses an autoencoder to encode a transmission signal into a low-dimensional latent vector to transmit the latent vector to an aircraft or vessel. The method also uses an autoencoder to decode the received latent vector to improve signal security and to reduce the amount of transmission information by approximately a factor of a hundred compared to the conventional method. In addition, a denoising autoencoder, which reduces ambient noises in the reconstructed outputs while maintaining the merit of the proposed autoencoder, is also proposed. To evaluate the performance of the proposed autoencoders, we simulated a bistatic active and a passive sonobuoy environments. As a result of analyzing the sample spectrograms of the reconstructed outputs and mean square errors between original and reconstructed signals, we confirmed that the original signal could be restored from a low-dimensional latent vector by using the proposed autoencoder within approximately 4% errors. Furthermore, we verified that the proposed denoising autoencoder reduces ambient noise successfully by comparing spectrograms and by measuring the overall signal-to-noise ratio and the log-spectral distance of noisy input and reconstructed output signals.

摘要

声呐浮标是一种一次性使用的设备,用于收集水下声学信息,其设计目的是将在特定区域收集到的信号传输给附近的飞机或船只,并在任务完成后沉入海底。在传统的声呐浮标信号收发系统中,收集到的信号采用诸如频分调制或高斯频移键控等技术进行调制和传输,然后由飞机或船只进行接收和解调。然而,这种方法存在信息传输量大且安全性低的缺点,因为其调制和解调方法相对简单。因此,在本文中,我们提出了一种方法,即使用自动编码器将传输信号编码为低维潜在向量,然后将该潜在向量传输给飞机或船只。该方法还使用自动编码器对接收到的潜在向量进行解码,以提高信号安全性,并与传统方法相比将传输信息量减少约一百倍。此外,还提出了一种去噪自动编码器,它在保持所提出的自动编码器优点的同时,减少了重建输出中的环境噪声。为了评估所提出的自动编码器的性能,我们模拟了双基地有源和无源声呐浮标环境。通过分析重建输出的样本频谱图以及原始信号与重建信号之间的均方误差,我们确认使用所提出的自动编码器可以在大约4%的误差范围内从低维潜在向量中恢复原始信号。此外,通过比较频谱图以及测量噪声输入信号与重建输出信号的整体信噪比和对数谱距离,我们验证了所提出的去噪自动编码器成功地降低了环境噪声。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a4/9460657/900c93c51a0d/sensors-22-06510-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a4/9460657/5b129e73b1ac/sensors-22-06510-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a4/9460657/f7d036794c77/sensors-22-06510-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a4/9460657/7a926d7ad16e/sensors-22-06510-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a4/9460657/f7480dd57a5c/sensors-22-06510-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a4/9460657/a2d2e3d7f1f0/sensors-22-06510-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a4/9460657/0e693766fe72/sensors-22-06510-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a4/9460657/0935427e1431/sensors-22-06510-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a4/9460657/f8b16ce21434/sensors-22-06510-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a4/9460657/14131d74572f/sensors-22-06510-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a4/9460657/900c93c51a0d/sensors-22-06510-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a4/9460657/5b129e73b1ac/sensors-22-06510-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a4/9460657/f7d036794c77/sensors-22-06510-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a4/9460657/7a926d7ad16e/sensors-22-06510-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a4/9460657/f7480dd57a5c/sensors-22-06510-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a4/9460657/a2d2e3d7f1f0/sensors-22-06510-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a4/9460657/0e693766fe72/sensors-22-06510-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a4/9460657/0935427e1431/sensors-22-06510-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a4/9460657/f8b16ce21434/sensors-22-06510-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a4/9460657/14131d74572f/sensors-22-06510-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a4/9460657/900c93c51a0d/sensors-22-06510-g010.jpg

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