An algorithm for 252Cf-Source-Driven neutron signal denoising based on Compressive Sensing

As photoelectrically detected 252Cf-source-driven neutron signals always contain noise, a denoising algorithm is proposed based on compressive sensing for the noised neutron signal. In the algorithm, Empirical Mode Decomposition（EMD） is applied to decompose the noised neutron signal and then find out the noised Intrinsic Mode Function（IMF） automatically. Thus, we only need to use the basis pursuit denoising（BPDN） algorithm to denoise these IMFs. For this reason, the proposed algorithm can be called EMDCSDN（Empirical Mode Decomposition Compressive Sensing Denoising）. In addition, five indicators are employed to evaluate the denoising effect. The results show that the EMDCSDN algorithm is more effective than the other denoising algorithms including BPDN. This study provides a new approach for signal denoising at the front-end.

由于光电探测到的锎 - 252源驱动的中子信号总是包含噪声，针对含噪中子信号提出了一种基于压缩感知的去噪算法。在该算法中，应用经验模态分解（EMD）对含噪中子信号进行分解，然后自动找出含噪的本征模态函数（IMF）。因此，我们只需使用基追踪去噪（BPDN）算法对这些IMF进行去噪。基于此原因，所提出的算法可称为EMDCSDN（经验模态分解压缩感知去噪）。此外，采用五个指标来评估去噪效果。结果表明，EMDCSDN算法比包括BPDN在内的其他去噪算法更有效。这项研究为前端的信号去噪提供了一种新方法。