基于ResNet的扩散频谱代码的周期估计
Han-Qing Gu1, Xia-Xia Liu1, Lu Xu1
1School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
本研究引入了一种新的CNN-ResNet方法,用于在复杂环境中准确估计直接序列扩散频谱 (DSSS) 信号参数. 该方法有效地识别了非合作信号的伪噪声 (PN) 代码周期.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 电磁战争 电磁战争
背景情况:
- 在复杂的电磁环境中,准确的信号识别和参数估计对于非合作信号监控至关重要.
- 直接序列扩散频谱 (DSSS) 信号分析的传统方法面临诸如峰值能量泄漏和虚假峰值干扰等挑战.
研究的目的:
- 开发一种高效,准确的实时方法来检测DSSS信号,并在非合作场景中估计其参数.
- 为了解决传统的时间延迟相关算法的局限性.
主要方法:
- 基于残余网络 (ResNet) 的一维 (1D) 卷积神经网络 (CNN) 用于伪噪声 (PN) 代码周期估计.
- 该方法将PN代码期估计作为扩散频谱代码长度估计的多分类问题.
- DSSS信号的In-phase/Quadrature (I/Q) 数据直接输入到CNN-ResNet模型中进行自动特征学习.
主要成果:
- 该CNN-ResNet模型有效估计了DSSS信号的PN代码长度,通过各种信号噪声比 (SNR) 从-20到10dB.
- 使用二进制相位变换键 (BPSK) 调制信号验证性能,并使用方程相位变换键 (QPSK) 调制信号进行测试.
- 分析指标包括损失函数,准确性,回忆率和混矩阵,展示了强大的概括能力.
结论:
- 拟议的1D CNN-ResNet方法准确地估计了非合作的DSSS信号的PN代码周期.
- 这种方法为传统算法提供了强大而有效的替代方案,特别是在具有挑战性的信号环境中.
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