基于最大熵原理的激光诱导荧光诊断解卷积算法研究

Qingyun Lei, Xiong Yang, M. Cheng, Fan Zhang, D. Guo, Xiaokang Li, Wenjie Xiao
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引用次数: 0

摘要

激光诱导荧光(LIF)光谱用于等离子体诊断,需要利用解卷积算法将多普勒效应从原始光谱信号中分离出来。然而,在存在噪声的情况下,直接解卷积会导致高频噪声成分无限放大,因而变得无效。为了解决这个问题,我们提出了一种基于最大熵原理的解卷积算法。我们利用各种噪声水平(信噪比,SNR = 20-80 dB)下的模拟 LIF 频谱和以 Xe 为工作流体的测量 LIF 频谱,验证了所提算法的有效性。在典型的实测光谱(信噪比 = 26.23 dB)实验中,与高斯滤波器和理查森-卢西(R-L)算法相比,所提出的算法的信噪比分别提高了 1.39 dB 和 4.66 dB,均方根误差(RMSE)分别降低了 35% 和 64%。此外,频谱角 (SA) 分别减少了 0.05 和 0.11。在高质量频谱(信噪比 = 43.96 dB)实验中,结果表明与 R-L 迭代算法相比,拟议算法的运行时间缩短了约 98%。此外,最大熵算法避免了参数优化设置,更适合自动执行。总之,所提出的算法既能准确解析多普勒频谱细节,又能有效抑制噪声,从而凸显了它在 LIF 频谱解卷积应用中的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on the deconvolution algorithm for the laser-induced fluorescence diagnosis based on the maximum entropy principle
Laser-induced fluorescence (LIF) spectroscopy is employed for plasma diagnosis, necessitating the utilization of deconvolution algorithms to isolate the Doppler effect from the raw spectral signal. However, direct deconvolution becomes invalid in the presence of noise as it leads to infinite amplification of high-frequency noise components. To address this issue, we propose a deconvolution algorithm based on the maximum entropy principle. We validate the effectiveness of the proposed algorithm by utilizing simulated LIF spectra at various noise levels (signal-to-noise ratio, SNR = 20–80 dB) and measured LIF spectra with Xe as the working fluid. In the typical measured spectrum (SNR = 26.23 dB) experiment, compared with the Gaussian filter and the Richardson-Lucy (R-L) algorithm, the proposed algorithm demonstrates an increase in SNR by 1.39 dB and 4.66 dB respectively, along with a reduction in root-mean-square error (RMSE) by 35% and 64% respectively. Additionally, there is a decrease in spectral angle (SA) by 0.05 and 0.11 respectively. In the high quality spectrum (SNR = 43.96 dB) experiment, the results show that the running time of the proposed algorithm is reduced by about 98% compared with the R-L iterative algorithm. Moreover, the maximum entropy algorithm avoids parameter optimization settings and is more suitable for automatic implementation. In conclusion, the proposed algorithm can accurately resolve Doppler spectrum details while effectively suppressing noise, thus highlighting its advantage in LIF spectral deconvolution applications.
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