卡尔曼滤波在道路动态响应数据处理中的应用研究

摘要

In order to solve the problem of data validity in road dynamic response and reduce the noise of acquisition signal,the practical application effects of mean filter,low-pass filter and Kalman filter are compared and studied. Based on the noise signal time-frequency analysis to determine the peak size and signal spectrum characteristics of noise signal;the filtering of these three methods is carried out;according to the signal-to-noise ratio,peak value and peak delay time of these 3 important indexes,illustrated by Kalman filtering algorithm in road dynamic response data filtering is better than the other two algorithms. After obtaining the massive data,by further optimizing the prediction model of the Kalman filter algorithm,it will be able to make its effect better and more significant.%为解决道路动态响应数据的有效性问题,降低采集信号的噪声,对比研究了均值滤波、低通滤波以及卡尔曼滤波算法的实际应用效果.通过对噪声信号进行时频分析,确定了噪声信号的峰值大小及信号频谱特征;进行了这3种方法的滤波;根据信噪比、峰值及峰值滞后时间这3个重要指标结果,说明卡尔曼滤波算法在道路动态响应数据滤波处理上要优于其他两种算法.在获取海量数据后,通过进一步优化卡尔曼滤波算法中的预估模型,将能够使其效果更佳显著.