一种机场环境光纤预警系统的信号识别新算法

A new method for signal recognition of the fiber-optic alarm system around airport

提出了一种基于导数分析与典型特征选取的信号识别新算法。首先,分析信号导数与频率的关系,得到信号的时频特性;其次,根据时频分布的特点,选取具有代表性的典型特征,以降低特征维度,减少识别时间;最后,应用概率神经网络(PNN)对典型特征进行学习和分类。采用4种扰动信号对本文算法进行实验验证,平均正确识别率达95.7%,且识别时间小于0.23s。实验结果表明,本文算法能够快速准确地识别扰动类型,为Mech-Zehnder(M-Z)干涉型周界系统的模式识别提供一种科学可靠的方法。

A new signal recognition method is presented based on the derivative analysis and feature extraction.Firstly,the time-frequency characteristics are obtained thought analyzing the relation of derivative and fr equency.Secondly,according to the time-frequency distribution characteristic,several typical features are extracted to reduce fea ture dimensions and recognition time. Finally,probability neural network （PNN） is used to identify and learn.The algor ithm is verified by four disturbance signals, the average accuracy rate reaches 95.7%,and the recognition time is less than 0. 23s.The experimental results show that the method can identify the type of disturbance quickly and accurately,and provide a scientific and reliable method for the pattern recognition of perimeter system based on Mach-Zehnder （M -Z） interferometer.