基于遗传算法优化卡尔曼滤波模型的GNSS CORS监测数据处理研究

Research on GNSS CORS Monitoring Data Processing Based on Genetic Algorithm Optimizing Kalman Filter Model

针对矿区GNSS CORS自动化监测数据波动大且含有测量噪声的特性,提出基于小波去噪的遗传算法优化Kalman滤波模型。首先利用小波阈值去噪法剔除原始数据中大部分高频噪声与粗差,再引入遗传算法对Kalman滤波中的过程噪声和量测噪声进行优化,实现噪声自适应寻优。实验结果表明,该组合模型平均相对误差为0.169 mm,均方误差为0.042 mm,相比于标准的Kalman滤波模型及方差补偿自适应Kalman滤波模型,具有更高的预测精度,能更好应用于矿区沉降监测。

Aiming at the characteristics of large fluctuation and measurement noise of the GNSS CORS automatic monitoring data in the mining area,a genetic algorithm based on wavelet denoising is proposed to optimize the Kalman filtering model.First,the wavelet threshold denoising method is used to remove most of the high-frequency noise and gross errors in the original data,and the genetic algorithm is introduced to optimize the process noise and measurement noise in the Kalman filter to achieve the adaptive optimization of the noise.The experimental results show that the average relative error of the combined model is 0.169 mm,and the mean square error is 0.042 mm.Compared with the standard Kalman filter model and the variance compensation adaptive Kalman filter model,it has higher prediction accuracy and can be better applied to mining subsidence monitoring.