用分数域模糊函数提升信号分类准确率
New Fractional Ambiguity Function Integrated with CNN-Based Machine Learning for Signal Classification
- 提出新型分数阶模糊函数，增强时频分辨率
- 在模拟数据上分类准确率显著优于传统方法
- 适合信号处理与机器学习交叉领域的研究者
本文提出一种基于分数傅里叶变换的新分数阶模糊函数（NFrAF），作为经典模糊函数的推广。严格推导了其对称性、边缘性及莫伊尔型恒等式等基本分析性质。通过验证其对单分量与多分量线性调频（LFM）信号的检测与定位能力，将NFrAF嵌入卷积神经网络机器学习框架用于信号分类。得益于其优异的时频分辨力和局部化特性，NFrAF提供的输入表示比短时傅里叶谱和经典模糊函数更具信息量。在模拟数据集上的实验表明，分类准确率持续提升，验证了该表示在数据驱动信号分析中的有效性。
原文摘要 · Abstract (English)
A new fractional ambiguity function (NFrAF) derived from the fractional Fourier transform is introduced as a generalization of the classical ambiguity function. The fundamental analytical properties of the NFrAF, including symmetry, marginality, and Moyal type identities, are rigorously established. After verifying its ability to detect and localize monocomponent and multicomponent linear frequency modulated (LFM) signals, the NFrAF is integrated into a convolutional neural network based machine learning framework for signal classification. Owing to its superior time frequency resolution and localization, the NFrAF provides a more informative input representation than conventional methods such as the spectrogram and classical ambiguity function. Experimental results on simulated datasets demonstrate consistent improvements in classification accuracy, highlighting the effectiveness of the proposed representation for data driven signal analysis.
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