融合小波变换和自适应Kalman滤波在GPS监测中的应用研究  

Application and Research on GPS Monitoring Based on Integrating Wavelet Transform and Adaptive Kalman Filter

作　　者：李炎寅 LI Yanyin(Suzhou Industrial Park Surveying,Mapping and Geoinformation Co.,Ltd.,Suzhou 215000,China)

机构地区：[1]苏州工业园区测绘地理信息有限公司

出　　处：《测绘与空间地理信息》2020年第1期89-92,共4页Geomatics & Spatial Information Technology

摘　　要：针对自适应卡尔曼滤波只适用于滤除高斯分布的白噪声,本文提出了融合小波变换和自适应卡尔曼滤波的算法。该算法利用小波变换的多尺度分解,将GPS高频的监测时间序列进行多层分解,重构出新的GPS监测时间序列,将其作为新的自适应卡尔曼滤波初始值,进行滤波处理。将融合算法的滤波结果与单一的自适应卡尔曼滤波结果进行对比分析,结果表明融合算法的滤波效果较为显著。同时,对融合算法滤除的噪声信息进行统计分析,结果表明融合算法滤除的噪声符合正态分布,进一步说明了该融合算法的有效性,为GPS的高频率、高精度的监测提供了技术支持。This paper proposed an algorithm,which integrated wavelet transform and adaptive Kalman filter for the lack of adaptive Kalman filter only filtering Gaussian distribution.The integrated algorithm decomposed in multi-scale the GPS high frequency monitoring information by using the wavelet transform and reconstructed the new GPS time series.Then it filtered the new GPS time series in adaptive Kalman filter.The paper compared the integrated algorithm filtered result with the single adaptive Kalman filtered result.The compared result showed that the filtered result was better on filtering noise in GPS time series.The statistical results of filtered noise showed that the noise of the integrated algorithm is accord with the normal distribution,which further proves the effectiveness of the integrated algorithm and provided technical support for GPS high frequency and high precision monitoring.

关 键 词：小波变换 自适应卡尔曼滤波 GPS监测 直方图统计 

分 类 号：P228[天文地球—大地测量学与测量工程]