| 脉冲噪声环境下水声通信信号调制识别方法 |
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| 引用本文： | 王彬,王海旺,李勇斌.脉冲噪声环境下水声通信信号调制识别方法[J].信号处理,2020,36(12):2107-2115. |
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| 作者姓名： | 王彬 王海旺 李勇斌 |
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| 作者单位： | 中国人民解放军战略支援部队信息工程大学信息系统工程学院 |
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| 基金项目： | 国家自然科学基金（61602511） |
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| 摘 要： | 为了提高浅海脉冲噪声环境下水声通信信号调制识别的性能和实用性，提出了基于降噪自编码器和卷积神经网络的调制识别方法。首先，利用降噪自编码器对含噪声信号进行降噪处理，然后，利用卷积神经网络对降噪信号的功率谱图进行分类，从而完成调制识别。此外，采用数据迁移思想构造训练数据集对网络进行训练解决了目标水域数据样本不足的问题。仿真实验和实际信号测试结果验证了本文方法的有效性。与现有算法相比，具有较高的识别率，并且提升了目标信道数据不足条件下的识别性能。
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| 关 键 词： | 调制识别 脉冲噪声 降噪自编码器 卷积神经网络 数据迁移 |
| 收稿时间： | 2020-10-12 |
Method of Modulation Recognition Technology for Underwater Acoustic Communication Signal in Impulsive Noise Environment |
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| Affiliation: | School of Information Systems Engineering, PLA Strategic Support Force Information Engineering University |
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| Abstract: | To improve the performance and applicability of modulation classification of underwater acoustic communication signal in impulse noise environment of shallow sea, an approach based on denoising automatic-encoder (DAE) and convolutional neural network (CNN) modulation recognition is proposed. First, a DAE-based noise reduction module is built to reduce the effects of the alpha stable distribution noise on modulation characteristics; Second, the power spectrum of the output signal is recognized by CNN, so as to complete the modulation recognition. Meanwhile, the idea of data migration is used to solve the problem that small sample target data cannot support neural network training. Simulation results and practical signal test results demonstrate the effectiveness of the proposed method. Compared with the existing algorithms, the proposed method reduces the requirement of professional knowledge, improves the recognition rate in impulse noise environment, and still has a better recognition rate under the condition of small sample target data. |
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