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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.
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Key words:
- gamma-ray spectrum /
- identification /
- relative entropy /
- spectrum peak /
- statistic fluctuation
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Table 1. Influence of repeated measurements
repeat times cosine of the angle distance of l1-norm relative entropy /10-4 2 0.996 0 0.020 9 3.630 1 3 0.996 7 0.012 1 3.410 5 4 0.995 6 0.026 8 1.529 0 5 0.999 4 0.018 7 4.830 6 6 0.999 5 0.022 5 6.557 9 mean 0.997 4 0.020 9 4.000 0 SD 0.001 7 0.005 1 2.000 0 
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Table 2. Influence of detecting distance
detecting distance/cm cosine of the angle relative entropy/10-4 5 0.996 6 32.000 0 10 0.997 3 6.964 8 20 0.997 7 3.998 5 50 0.965 4 33.000 0 100 0.815 9 133.000 0 mean 0.962 1 35.000 0 SD 0.072 8 50.000 0
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Table 3. Influence of absorption materials
material thickness /mm distance of l1 norm relative entropy Cu 1 0.055 1 0.017 2 Cu 2 0.096 4 0.018 9 Cu 3 0.142 7 0.019 4 W 1 0.436 4 0.593 0 W 2 0.582 4 0.597 6 W 3 0.648 3 0.609 5
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