The wireless signal recognition plays a crucial role in the wireless signal transmission and monitoring. In order to reduce the effect of various interference sources and white noise on transmission signal,and improve the signal recognition accu-racy,a feature extraction technology based on principal component analysis(PCA)and wavelet transform method is analyzed, and a model to extract the characteristics of received signal with integral envelope method is proposed. The close degree among the envelopes of different signal samples is used to construct the concise and specific evaluation indexes,and verify its validity. The fuzzy mathematics identification function is used to calculate the close degree between the sample and regional division. The wireless signal is judged and identified according to the difference value of the close degree. The experimental results show that the algorithm has high recognition performance,high recognition rate,perfect robustness,and low computational complexity.%无线信号识别在无线信号传输和监测中占有重要地位,为了减少各类干扰源和白噪声对传播信号造成的影响,提高信号识别准确度,分析了基于PCA和小波变换法的特征提取技术,提出积分包络法来提取接收信号特征的模型,采用不同信号样本包络之间的贴近度构建简洁而明确的评价指标以验证有效性,同时利用模糊数学识别功能计算样本与区域划分之间的贴近度,通过贴近度差值来判断和识别无线信号.实验结果表明,该算法识别性能较好,不仅具有较高的识别率和良好的稳健性且计算复杂度较低.
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