| 袁博,钱鹏,赵猛,杨馥锦,鹿力成.信号分离在深海定位中的应用[J].,2025,44(1):155-161 |
| 信号分离在深海定位中的应用 |
| Application of signal separation in deep-sea positioning |
| 投稿时间：2023-09-05 修订日期：2024-12-30 |
| 中文摘要: |
| 声波在深海中远距离传播时海水吸收、扩展导致 传播损失大，接收到的声波能量非常小，同时受到航船风浪等强噪声干扰，声波信号的信噪比非常低。在低信噪比的情况下，信号增强、信号降噪等数据处理方法的效果均降低，对水下目标定位、检测和识别造成很大影响。该本文针对水下目标低信噪比定位问题，应用全卷积时域网络，基于信号幅度和相位的解耦，提出了一种快速信噪分离方法。该方法利用了端到端时域分离的深度学习框架，通过线性编码器编码信号，编码之后的信号波形可以通过一组加权函数分离出信号和噪声，最后再使用线性编码器将分离后的信号反转到时域进行目标定位。通过数据仿真验证了本该方法的可行性，并对海上实验数据进行处理，取得较好结果。 |
| 英文摘要: |
| The propagation of sound waves in deep sea experiences significant losses due to absorption and spreading, resulting in very small received sound energy. Additionally, strong noise interference from ships and waves leads to a low signal-to-noise ratio (SNR) in the sound wave signals. In conditions of low SNR, the effectiveness of signal enhancement and noise reduction methods diminishes, which greatly impacts underwater target localization, detection, and recognition. This paper addresses the issue of low SNR localization for underwater targets by applying a fully convolutional time-domain network and proposing a rapid signal-noise separation method based on the decoupling of signal amplitude and phase. The method utilizes an end-to-end time-domain separation deep learning framework, where a linear encoder encodes the signal. The encoded signal waveform can be separated into signal and noise using a set of weighted functions. Finally, the separated signal is inverted back to the time domain using the linear encoder for target localization. The feasibility of this method is validated through data simulation, and it is applied to process experimental data from the sea, yielding favorable results. |
| DOI：10.11684/j.issn.1000-310X.2025.01.016 |
| 中文关键词: 信噪分离 深海定位 全卷积时域网络 |
| 英文关键词: signal-to-noise separation deep-sea positioning fully convolutional time-domain network |
| 基金项目: |
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