基于数字摄影技术的动态变形数据的卡尔曼滤波分析

为了进一步减弱非量测数码相机噪声对测量精度的影响，提高基于数字摄影技术的工程结构动态变形监测精度，采用标准卡尔曼滤波、方差补偿卡尔曼滤波、极大验后卡尔曼滤波和方差分量卡尔曼滤波分别处理了桥梁弹性大变形数据，研究卡尔曼滤波在处理动态变形数据噪声中的适应性，并进一步量化了方差分量卡尔曼滤波在数据处理中的优势。研究发现，方差分量卡尔曼滤波噪声处理较为稳定，且误差较小。该方法不仅适用于基于数字摄影技术的动态弹性小变形噪声处理，在处理基于数字摄影技术的桥梁动态弹性大变形噪声时，同样具有较好的效果。室内相似材料模型试验研究表明，经过方差分量卡尔曼滤波进行数据处理后，测量误差小于0.5 mm，能够满足变形监测的精度要求。

Analysis of Kalman filter in dynamic deformation data in digital photography

In this study, to reduce the impact of non-measureable noise on the measurement accuracy and to improve the monitoring accuracy of an engineering structure′s dynamic deformation in digital photography, the standard Kalman filter, variance-compensated Kalman filter, maximum posterior Kalman filter, and variance component Kalman filter are used to deal with the bridge elastic large deformation data; the Kalman filtering adaptability in dealing with dynamic deformation data is studied; and the advantages of employing variance component Kalman filter in data processing are quantified. It is found that the noise processing of variance component Kalman filter is stable and the error is small. This method is suitable for the processing of dynamic elastic small deformation noise in digital photography and exhibits good results in the processing of dynamic elastic large deformation noise in digital photography. Laboratory experiments on similar materials show that after data processing via variance component Kalman filter, the measurement error is less than 0.5 mm, which meets the accuracy requirement of deformation monitoring.