位移和加速度融合的自适应多速率Kalman滤波方法  

A new adaptive multi-rate Kalman filter for the data fusion of displacement and acceleration

作　　者：林旭[1,2] 罗志才[2,3,4] 

机构地区：[1]成都理工大学地球科学学院,成都610059 [2]武汉大学测绘学院,武汉430079 [3]武汉大学地球空间环境与大地测量教育部重点实验室,武汉430079 [4]测绘遥感信息工程国家重点实验室,武汉430079

出　　处：《地球物理学报》2016年第5期1608-1615,共8页Chinese Journal of Geophysics

基　　金：国家重点基础研究发展计划(973计划)(2013CB733302);国家自然科学基金项目(41174062;41131067);中央高校基本科研业务费专项资金项目(2012214020206);地球空间环境与大地测量教育部重点实验室开放基金(12-02-09)联合资助

摘　　要：多速率Kalman滤波方法可用于低采样率的位移和高采样率的加速度数据融合,而未知的噪声协方差信息则显著制约着多速率Kalman滤波精度.本文通过将多速率Kalman滤波转换为传统的单速率Kalman滤波,建立了Kalman滤波增益的自协方差矢量与未知的加速度谱密度和观测噪声参数间的线性函数模型,并采用最小二乘估计方法对未知的噪声协方差参数进行估计,进而有效地提高了多速率Kalman滤波精度.数值仿真和震动台实验结果验证了本文方法的正确性和有效性.The multi-rate Kalman filter can be used for the data fusion of displacement and acceleration,which were sampled at different frequencies. However,the noise covariance matrices,especially the process noise covariance matrix,are usually unavailable in the practical applications.With inappropriate noise covariance matrices,the state estimates of multi-rate Kalman filter is suboptimal.In this paper,a new adaptive multi-rate Kalman filter,which is based on the autocovariance least-squares method,is proposed.For a given set of displacement and acceleration data sampled at different frequencies,the data fusion problem is formulated as the single-rate Kalman filter rather than the multi-rate Kalman filter.And the correlations between the innovations were used to establish a relationship to the unknown parameters aboutthe noise covariance matrices.Therefore,the unknown parameters can be estimated by solving the least-squares problem.The validity of the proposed method is demonstrated by a numerical example and an earthquake engineering test from the Large High-Performance Outdoor Shake Table.

关 键 词：多速率采样 位移 加速度 自适应Kalman滤波 自协方差最小二乘法 RTS平滑 

分 类 号：P223[天文地球—大地测量学与测量工程]