多星座组合导航自适应联合卡尔曼滤波算法研究

Adaptive Kalman Filtering for Multi-constellation Integrated Navigation System

针对多星座卫星组合导航,提出了一种自适应联合卡尔曼滤波算法,采用描述机动载体运动的"当前"统计模型,直接从各卫星导航系统接收机输出的定位信息入手,将各种误差因素的影响等效为一个总误差,建立一种动态定位的自适应卡尔曼滤波模型。为了进一步提高滤波器的动态性能,通过引入调整系数、加权因子和自适应调节量对自适应滤波算法进行了改进,并分别对GPS、GLONASS和GALILEO系统设计了自适应子滤波器,然后采用联合滤波算法对各个子滤波器进行数据融合处理,最后对GPS/GLONASS/GALILEO组合导航系统进行了仿真验证,结果表明,该算法增强了滤波器的跟踪能力,改善了滤波效果,提高了定位精度。

A muhi-constellation integrated navigation adaptive federated Kahnan filtering algorithm is put forward in this paper. Assuming a current statistical model for maneuvering targets and considering that errors caused by different error sources ean be equivalent to a total error of positioning results fi＇om the receivers of each satellite navigation system, an adaptive Kalman filtering model in kinematic positioning is presented. In order to improve the performance of kinematic positioning filter, a modified adaptive filtering algorithm is proposed by means of introducing adjustment coefficient, weighted factor and adaptive regulating variable. Subfilters for GPS, GLONASS and GALILEO system are designed respectively; then data fusion processing is practiced on the subfihers by federated filtering algorithm; finally the simulation experiment is carried out on GPS/GLONASS/GALILEO multi-constellation integrated navigation system. The simulation results indicate that the tracking performance is enhanced, filtering effect is improved and positioning accuracy is increased.