闪电AR谱的多重分形特性分析及放电类型的识别

对闪电时域波形的分形研究由于忽略了其频率特性,致使复杂多变的闪电过程的全部特性无法得到充分表征。针对此问题,本文将多重分形理论引入到现代谱估计中,提出了一种基于AR(auto-regressive)谱的闪电电场信号的多重分形特性分析及放电类型的识别方法。首先基于AR模型谱估计法获得闪电电场信号的功率谱,然后,通过多重分形去趋势波动分析(multifractal detrended fluctuation analysis,MF-DFA) 法验证了闪电AR谱序列具有多重分形特性,并进一步对AR谱序列的Hurst指数以及多重分形谱进行了讨论,最后将相关参数作为闪电信号的有效特征值输入支持向量机进行了云闪(intracloud lightning) 和地闪(cloud-to-ground lightning,CG) 不同放电类型的识别。实验结果表明,本文方法对云、地闪信号的有效识别率达到了94%以上,该研究成果对闪电的特性研究与自动化识别技术均具有一定的参考价值。

Analysis of multifractal characteristics of lightning AR spectrum and discharge type identification

The fractal study of the lightning time domain waveform ignores its frequency characteristic,so that all the characteristics can not be fully characterized.In order to solve this problem,this paper introduces the multifractal theory into modern spectral estimation,and proposes a multifractal characteristic analysis and discharge type identification method of the lightning electric field signal based on auto-regressive (AR) spectrum.Firstly,the power spectrum of the lightning electric field signal is obtained based on the AR model spectrum estimation method.Then the multifractal detrended fluctuation analysis (MF-DFA) method is used to verify that the lightning AR spectrum sequence has multifractal characteristics,and the Hurst exponent and multifractal spectrum of AR spectrum sequence are further discussed.Finally,these parameters are input into support vector machine as the effective eigenvalues to identify different discharge types of intracloud lightning (IC) and cloud-to-ground lightning (CG).The experimental results show that the effective recognition rate of the proposed method reaches more than 94%.The research results have certain reference value for the research of lightning characteristics and automatic recognition technology.