不同的自适应卡尔曼滤波在地铁隧道沉降监测数据处理中的应用研究

Study on Application of Different Self-adapting Kalman Filtering Methods in Data Processing in Settlement Prediction of Metro Tunnels

由于传统卡尔曼滤波所建立的数学模型不是很精确,且动态噪声统计特性不易确定,可能导致滤波发散而无法获得准确的预测结果。为了克服这种现象,提出自适应卡尔曼滤波方法。分别用卡尔曼滤波、基于极大验后估计原理的自适应卡尔曼滤波和基于方差补偿的自适应卡尔曼滤波在地铁隧道沉降监测数据处理中的应用进行分析比较,结果表明,与其他方法相比,基于方差补偿的自适应卡尔曼滤波方法的变形预测精度更高。

As the mathematical model that is built in traditional Kalman filtering is not very accurate and its statistical characteristics of dynamic noise are difficult to confirm,the traditional Kalman filtering may lead to filtering divergence, even may lead to evaluation distortion.Therefore,the method of self-adapting Kalman filtering is put forward to solve this problem.In this article,traditional Kalman filtering,self-adapting Kalman filtering based on maximum a posterior （MAP）estimation principle,and self-adapting Kalman filtering based on variance compensation are used to process the data in settlement prediction of Metro tunnels,and analysis and comparison are made among these three methods.The result shows that,compared to the other two methods,the method of self-adapting Kalman filtering based on variance compensation is more accurate in settlement prediction.