强激光与粒子束, 2018, 30 (10): 106003, 网络出版: 2018-11-25
基于信息理论的γ能谱辨识新方法
Novel information theory based method of gamma-ray spectra identification
γ能谱 辨识 相对熵 谱峰 统计浮动 gamma-ray spectrum identification relative entropy spectrum peak statistic fluctuation
摘要
提出了一种基于相对熵的放射源γ能谱识别方法。首先, 利用主成分分析(PCA)算法压缩数据, 构造γ射线能谱的特征空间。然后, 采用随机化技术(RT)来使特征空间中γ射线能谱的特征值归一化, 这样, γ射线能谱的特征空间可以看作是概率空间。最后, 定义两个概率空间的相对熵来测量两个γ射线能谱的相对差异。大量实验表明, 所提方法能够更加有效地辨识γ射线能谱, 不仅计算量小, 而且对诸如统计浮动、谱峰偏移、底噪等因素具有很高的鲁棒性。
Abstract
In this paper, a relative entropy based method is proposed to identify the gamma-ray spectra of radioactive sources. Firstly, Principal Component Analysis (PCA) algorithm is used to compress data and construct an eigenspace of the gamma-ray spectrum. Then, Randomization Technique (RT) is adopted to normalize the eigenvalue of the gamma-ray spectrum in eigenspace. Hence, the eigenspaces of gamma-ray spectra can be regarded as probability spaces. Finally, the relative entropy of two probability spaces is defined to measure the difference between two contrasted gamma-ray spectra. It was experimentally demonstrated that the proposed method could perform better judgment about the identity of two gamma-ray spectra over most existing methods. The proposed method has the characteristics of less calculation and higher robustness for impact factors of statistic fluctuations, peaks drift and background.
印茂伟, 任雪美, 廖鹏, 任立学. 基于信息理论的γ能谱辨识新方法[J]. 强激光与粒子束, 2018, 30(10): 106003. Yin Maowei, Ren Xuemei, Liao Peng, Ren Lixue. Novel information theory based method of gamma-ray spectra identification[J]. High Power Laser and Particle Beams, 2018, 30(10): 106003.