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超声波通过金属通信基于深度学习辅助的回声取消

Jinya Zhang¹, Min Jiang², Jingyi Zhang²

¹Engineering Training Center, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.

Sensors (Basel, Switzerland)

|April 13, 2024

概括

一个新的深度学习算法,双路径循环神经网络 (DPRNN),有效地取消超声波通过金属通信中的回声. 这种先进的回声取消显著提高信号质量,可靠的数据传输.

科学领域:

声学声学在声学上
信号处理信号处理
机器学习机器学习

背景情况:

超声波通过金属屏障提供高效的无线传输,绕过法拉第屏蔽的限制.
超声波通道中的回声对高质量的通信构成重大挑战,通常由通道均等器或预扭曲波器来解决.

研究的目的:

调查深度学习算法的有效性,双路循环神经网络 (DPRNN),用于超声波通过金属通信系统中的回声取消.

主要方法:

使用超声波传感器和FPGA模块构建了一个硬件软件系统.
应用DPRNN算法对具有不同信号噪声比率 (SNR) 的信号,传输速率为2Mbps.
对LMS,RLS和PNLMS等传统方法进行性能评估.

主要成果:

在所有测试的信号噪声比率中,DPRNN方法实现了超过20dB的SNR改进.
通过50毫米板成功传输图像,获得了24.8dB的PSNR和95%的SSIM.
与LMS,RLS和PNLMS回声取消方法相比,DPRNN显示出更高的效率.

结论:

DPRNN算法是超声波通过金属传输中对回声取消的强大工具.
这种深度学习方法显著提高了超声波通信系统穿透金属屏障的性能和可靠性.

关键词:

这是深度学习.声回应取消取消的方法超声波通信超声波通信

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