针对被采样的超弱光纤光栅（FBG）反射光谱含有干扰噪声的问题，提出一种应用于大容量超弱传感网络的高速寻峰算法。该算法引入权重因子加入到最小二乘拟合算法实现加权最小二乘拟合(WLS) 算法，对高斯曲线拟合系数进行优化，定位出中心波长，然后再通过非对称高斯修正(AG)对定位的中心波长进行修正，提出WLS结合AG（WLS-AG）的算法，实现抗噪声干扰高精度寻峰。通过实验，对比分析最小二乘拟合算法、质心算法、WLS算法及文章提出的WLS-AG算法分别在不同噪声下的峰值误差平均值，以及变温环境下误差平均值。实验结果表明，在高信噪比的情况下，WLS-AG算法连续20次重复性实验平均误差＜1 pm，在低信噪比的情况下，平均误差约为10 pm；在不同温度下的检测误差在1 pm内，且最为稳定，满足超弱FBG传感系统精度解调的要求。

Aiming at the interference of the sampled ultra-weak Fiber Bragg Grating (FBG) reflectance spectrum, a high-speed peak seeking algorithm for large capacity ultra-weak sensor networks is proposed. In this algorithm, weight factors are introduced into the least squares fitting method to realize the Weighted Least Squares fitting (WLS) algorithm with optimized fitting coefficient of gaussian curve to locate the center wavelength. Then the central wavelength is modified by Asymmetric Gaussian correction (AG) to achieve high-precision peak location against noise interference. We propose WLS combined with AG (WLS-AG) algorithm. Through experiments, the peak error averages of the least square fitting algorithm, centroid algorithm, WLS algorithm and the WLS-AG algorithm proposed in this paper are compared and analyzed under different noises. The experimental results show that the WLS-AG algorithm works with high Signal-to-Noise Ratio （SNR）. The average error of 20 consecutive repetitive experiments is less than 1 pm, and the mean error is about 10 pm at low SNR. The detection error at different temperatures is within 1pm, which is the most stable and meets the requirements of ultra-weak FBG sensor system for precision demodulation.