基于信息理论的γ能谱辨识新方法

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。

Novel information theory based method of gamma-ray spectra identification

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.