基于GPS/气压高度计的高度数据融合算法研究

Research on Height Data Fusion Algorithm Based on GPS/Barometric Altimeter

为了解决单独使用气压高度计或GPS测量高度时存在数据不稳定以及精度不够等问题,采用卡尔曼滤波算法对气压高度计与GPS测量高度数据进行数据融合。并借助于高处作业设备的运动矢量模型,建立卡尔曼滤波算法的数学模型,研究中给出该数据融合算法重要的推导过程。仿真结果表明,卡尔曼滤波能够提高数据的精度和容错性,为目前装有2种传感器的设备提供一种更优的数据处理方式,提高GPS/气压高度计组合测高系统的测量精度,满足在高处作业设备高度测量应用中的要求。

In order to solve the problem of data instability and insufficient precision when using barometric altimeter or GPS alone,the Kalman filter algorithm was used to fuse the barometric altimeter and GPS height measurement data.The mathematical model of the Kalman filter algorithm was established by means of the motion vector model of the suspended platform,and an important derivation process of the data fusion algorithm was given in the study.Simulation results demonstrated that the algorithm could improve accuracy and fault tolerance of the data,provide a better data processing method for platform equipped with two sensors,and satisfy the height measuring requirement of suspended platform.