ISSN 0253-2778

CN 34-1054/N

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Weak nuclear pulse signal extraction from intensive background noise

  • 1.

    School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China

  • 2.

    Department of Automation, University of Science and Technology of China, Hefei 230027, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2018.09.001
  • Received Date: 12 March 2018
  • Accepted Date: 27 September 2018
  • Rev Recd Date: 27 September 2018
  • Publish Date: 30 September 2018
    Abstract

  • Abstract

    It is a very challenging problem to extract the amplitude and occurring time of weak nuclear pulse signals in the existence of intensive background noise. To solve this problem, this paper proposes a pulse signal estimation method based on Gabor transform and sparse representation. Firstly, it builds a pulse signal representation dictionary through the Gabor decomposition of mononuclear pulse signal samples. Then it eliminates the fluctuation of the Gabor bases, which is caused by the detector variation and the measurement noise, by using K-SVD algorithm, and learns a self-consistent over-complete dictionary which is used to represent the useful signal being overwhelmed in the background noise. Finally, it reconstructs the desired signal by an improved OMP algorithm, greatly attenuates the noise and achieves the goal of extracting the weak nuclear pulse signal. The effectiveness and efficiency of the proposed method are verified through simulations and experiments on a CsI(Tl) detector. Results confirm that the proposed method outperforms the traditional Salley-Keys smoothing and Kalman filtering methods with smaller estimation errors of the amplitude and peak occurring time of the concerned nuclear pulse signal.

    Abstract

    It is a very challenging problem to extract the amplitude and occurring time of weak nuclear pulse signals in the existence of intensive background noise. To solve this problem, this paper proposes a pulse signal estimation method based on Gabor transform and sparse representation. Firstly, it builds a pulse signal representation dictionary through the Gabor decomposition of mononuclear pulse signal samples. Then it eliminates the fluctuation of the Gabor bases, which is caused by the detector variation and the measurement noise, by using K-SVD algorithm, and learns a self-consistent over-complete dictionary which is used to represent the useful signal being overwhelmed in the background noise. Finally, it reconstructs the desired signal by an improved OMP algorithm, greatly attenuates the noise and achieves the goal of extracting the weak nuclear pulse signal. The effectiveness and efficiency of the proposed method are verified through simulations and experiments on a CsI(Tl) detector. Results confirm that the proposed method outperforms the traditional Salley-Keys smoothing and Kalman filtering methods with smaller estimation errors of the amplitude and peak occurring time of the concerned nuclear pulse signal.
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    • References(16)
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    [1]
    曾晨浩, 冯孝杰, 段中山, 等. 基于模极大值法的γ能谱数据处理与分析[J]. 原子能科学技术, 2017, 51(7): 1305-1310.
    [2]
    向清沛. 基于序贯贝叶斯分析的放射性核素快速识别方法研究 [D].北京：中国工程物理研究院, 2014.
    [3]
    李伦辉, 何剑锋, 王芹, 等. 改进小波阈值方法对γ能谱去噪的研究 [J]. 原子能科学技术, 2016, 50(7): 1279 -1283.
    LI L H,HE J F, WANG Q, et al. Study of γ energy spectrum denoising-based on improved wavelet threshold method [J]. Atomic Energy Science and Technology, 2016, 50(7): 1279-1283.
    [4]
    高晋占. 微弱信号检测 [M]. 北京：清华大学出版社, 2011.
    [5]
    王坤朋, 柴毅, 苏春晓, 等. 新型微弱受激散射光能量信号检测方法 [J]. 中国激光, 2013, 40(3): 204-209.
    WANG K P, CHAI Y, SU C X, et al. Novel method for detection of weak stimulated scattering light energy signal [J]. Chinese J. Lasers, 2013, 40(3): 204-209.
    [6]
    MARTIN A, HARBISON S A. Radiation Detection and Measurement[M]. Proc. IEEE, 2010.
    [7]
    WANG K, CHAI Y, SU C. Sparsely corrupted stimulated scattering signals recovery by iterative reweighted continuous basis pursuit [J]. Rev. Sci. Instrum., 2013, 84(8): 339-624.
    [8]
    MARSHALL A, OLKIN I. Sparse Representation [M]. Springe, 2014.
    [9]
    MALLAT S G, ZHANG Z. Matching pursuits with time-frequency dictionaries [J]. IEEE Transactions on Signal Processing, 1993, 41(12): 3397-3415.
    [10]
    AHARON M, ELAD M, BRUCKSTEIN A. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation [J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322.
    [11]
    PATI Y C, REZAIIFAR R, KRISHNAPRASAD P S. Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition [C]// The Twenty-Seventh Asilomar Conference on Signals, Systems and Computers. Pacific, USA: IEEE Press, 1993: 40-44.
    [12]
    EDOARDO A, VIGGO K. On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems[J]. Theoretical Computer Science, 1998, 209(1-2): 237-260.
    [13]
    ENGAN K, AASE S O, HAKON H. Method of optimal directions for frame design [C]// IEEE International Conference on Acoustics, Speech, and Signal Processing. Phoenix, USA: IEEE Press, 1999: 2443-2446.
    [14]
    PEI S C, DING J J. Relations between gabor transforms and fractional fourier transforms and their applications for signal processing [J]. IEEE Transactions on Signal Processing, 2007, 55(10): 4839-4850.
    [15]
    LESAGE S, GRIBONVAL R, BIMBOT F, et al. Learning unions of orthonormal bases with thresholded singular value decomposition [C]// IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005, 293-296.
    [16]
    CHEN S S, DONOHO D L, SAUNDERS M A. Atomic decomposition by basis pursuit [J]. Society for Industrial and Applied Mathematics Philadephia, USA: IEEE Press, 1998, 43(1): 129-159.)
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