随机抽样一致性算法在激光光谱中的应用研究

Applications of random sample consistency algorithm on laser spectroscopy

为了解决波长调制激光光谱技术探测大气痕量气体浓度中信号处理算法的不足,提出了一种基于随机抽样一致性算法的气体浓度反演算法。以大气甲醛分子的仿真信号和实际测量信号为例,进行了理论分析和实验研究,并与传统的最小二乘法相比较。结果表明,该算法具有较强的抗噪声和异常点干扰能力,尤其是在低信噪比的条件下,精确度可提高1个量级,体现出较高的可靠性和优越性。

In order to solve the insufficient of signal process algorithms during the detection of atmospheric trace gas concentration by wavelength modulation laser spectroscopy technique, a new method of gas concentration inversion based on the random sample consistency （RANSAC） algorithm was proposed. By choosing the simulation signal and the actual measurement signal of formaldehyde in the atmosphere as examples, theoretical analysis and experimental study were carried out and compared with the traditional least square method. The results show that the proposed algorithm has better immunity to noises and outliers. Especially under the conditions of low signal-to-noise ratio （SNR） , the measurement accuracy can be improved by one order of magnitude. The algorithm shows better reliability and superiority.