将经验模态分解方法应用于光电探测的裂变中子事件的分析与处理中,通过引入基于二次平滑度检测的自适应的分量处理机制和基于小波阈值技术的分量去噪方法,构建了一种基于光电探测的中子裂变事件信号去噪处理新方法。模拟实验和实测数据的分析结果研究表明,在同等条件下,此方法适应性好,通过去噪处理后,信号的信噪比高,且信号的平滑度也较好,避免了因采取过度的去噪处理而将一些有用信息损失掉的不足。同时,去噪程度指标可进行量化设置。

The empirical mode decomposition (EMD) is applied to analysis and processing of the fission neutrons events based on photoelectric detection. A new model on the de-noise of fission neutrons signals is established with self-adapting double-smoothness-detecting (DSD) algorithm on intrinsic mode function (IMF),and wavelet threshold filter. With the simulated and survey data,a series of comparisons with other signal de-noise methods are made. On an equal footing,the EMD-based-DSD has a better adaptability on various fission neutrons signals. The signal-to-noise ratio of the treated signal is higher while the waveform is more even. The loss of useful information caused by over-de-noise is avoided. Moreover,the de-noise degree can be customized quantifiably.