基于周期静止特征和卷积神经网络的海上频谱传感
1College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
Entropy (Basel, Switzerland)
|August 28, 2025
概括
这项研究引入了一种使用周期静止特征和卷积神经网络 (CNN) 的新型海上频谱传感算法 (TC2NND). 这种方法在具有挑战性的海上环境中提高了检测准确性, 性能优于传统技术.
科学领域:
- 电气工程
- 计算机科学
- 信号处理
背景情况:
- 由于环境动态,海上认知无线电网络 (MCRN) 面临重大频谱传感 (SS) 挑战.
- 现有的SS方法难以应对海上环境的复杂性,包括海上运动和不稳定的链路.
研究的目的:
- 为MCRN开发一种适应性频谱传感算法,以解决环境复杂性.
- 提高海上频谱传感的准确性和可靠性.
主要方法:
- 一种以分类为导向的方法将SS转化为分类问题.
- 使用快速里埃转换 (FFT) 积累方法 (FAM) 提取周期静止特征.
- 使用卷积神经网络 (CNN) 来进行信号分类和检测 (TC2NND算法).
主要成果:
- 在TC2NND算法实现了91.5%的检测概率与5%的误报概率在-10dBSNR.
- 与海上环境中的传统频谱传感方法相比,其表现优越.
结论:
- 拟议的TC2NND算法为MCRN中的频谱传感提供了强大而有效的解决方案.
- 在动态的海上条件下,利用周期静止特征和CNN显著提高了SS的性能.
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