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- 저자 : Chunguang Ma (검색결과 2 건)
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The Recognition Method of Radiation Source Based on Information Entropy and Cloud Model
Yun Lin,Can Wang,Chunguang Ma,Zheng Dou,Zhiqiang Wu,Zhiping Zhang 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.2
Information entropy features are often used for radiation source signal recognition, but due to the information entropy is very sensitive to noise, so this method has greater recognition rate changes with the SNR. This paper putting forward a viable recognition based on Entropy and cloud model. using cloud model to extract secondary features of signals, build radiation source signal’s entropy and cloud feature vector. The method uses cloud model description and processing interval fuzzy and observation noise data, better solve the low SNR cases of radiation source signal feature extraction problem. At the same time, putting forward the similar cloud classification recognition algorithm based on cloud model. The simulation results show that Entropy and cloud model has better recognition effect under low SNR, which can improve the signals’ recognition rate under low SNR.
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Genetic Analysis of Generalized S-Transform
Yun Lin,Xiaowan Yu,Chunguang Ma,Zheng Dou,Zhiqiang Wu,Zhiping Zhang 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.4
This text starts with the short time Fourier transform and continuous wavelet transform to deduce the generalized S-transformation. From the point of generation views, we analyzed a relative relationship between generalized S-transformation and the short time Fourier transform, and the other relative relationship between generalized S transform and continuous wavelet transform. The article gives the definition of “the gene mutation of formula” and “the genetic restructuring of formula”, and introduces the deriving process of the two core concept. Theoretical analyses show that generalized S-transformation inherited the desirable characteristics in short time Fourier transform which use the window function to select suitable signal. Through genome sequencing of specific parameters, generalized S-transformation has a stronger adaptation that the time-frequency window could make real-time adjusting of frequency. Moreover, generalized S-transformation breaks out limitation that the wavelet function has to content the admissible conditions. From the point of gene mutation, we give the definition of “the gene mutation of formula”. Based on the structure form of wavelet functions, we define the generalized S-transformation with a wider domain of definition. Generalized S-transformation inherited the desirable characteristics of the short time Fourier transform and continuous wavelet transform. It has great utility and flexibility in analyzing non-stationary signals.
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