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A Detailed Evaluation of the Correlation-Based Method Used for Estimation of the Brillouin Frequency Shift in BOTDA Sensors

机译:用于估计BOTDA传感器中布里渊频移的基于相关方法的详细评估

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This paper thoroughly describes and evaluates the method that was previously presented for estimating the central frequency of noisy Lorentzian curves (spectra) acquired from the measurements with Brillouin optical time domain analysis (BOTDA) sensors. The estimator is based on the cross-correlation technique and addresses the problem of sensitivity to noise and parameter initialization observed in other central frequency estimation methods employed with BOTDA sensors. Most of the current estimation methods rely on optimized rigorous least squares or maximum likelihood estimation (MLE) algorithms, which are sensitive to the parameter initialization and noise as they iteratively attempt to minimize the squared error or maximize the matching probability between the model and noisy curve. Alternatively, the estimation made with the cross-correlation based method is more accurate, noniterative, and insensitive to the parameter initialization. This statement is demonstrated and proved by comparing the correlation-based method with two commonly used iterative curve fitting methods based on the Levenberg-Marquardt algorithm and MLE.
机译:本文彻底描述和评估了先前提出的方法,该方法用于估计从布里渊光学时域分析(BOTDA)传感器的测量结果中获得的嘈杂的洛伦兹曲线(频谱)的中心频率。估计器基于互相关技术,解决了对噪声的敏感度和参数初始化问题,该问题在BOTDA传感器采用的其他中心频率估计方法中观察到。当前大多数估计方法都依赖于经过优化的严格最小二乘法或最大似然估计(MLE)算法,这些算法对参数初始化和噪声敏感,因为它们反复尝试最小化平方误差或最大化模型与噪声曲线之间的匹配概率。可替代地,利用基于互相关的方法进行的估计更加准确,非迭代并且对参数初始化不敏感。通过将基于相关的方法与基于Levenberg-Marquardt算法和MLE的两种常用的迭代曲线拟合方法进行比较,证明并证明了这一说法。

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