INS/GPS/TAN系统中的联邦卡尔曼滤波信息融合方法

INFORMATION FUSION OF FEDERAL KALMAN FILTERING IN INS/GPS/TAN SYSTEMS

在分析联邦卡尔曼滤波原理和结构的基础上，提出了一个适合惯性导航系统／全球定位系统／地形辅助导航（ＩＮＳ／ＧＰＳ／ＴＡＮ）子系统状态向量维数不同这一特点的联邦卡尔曼滤波方法，并构造了一种具有较强容错性能和适应性的联邦卡尔曼子系统滤波器结构。由于采用了局部状态扩展、局部状态压缩、智能的不等间隔滤波、多级故障检测、阈值控制等信息融合技术，在保证精度的前提下，联邦卡尔曼滤波器的可靠性、适应性得到了明显提高。

On the basis of analyzing the fundamental theory and structure of Federal Kalman Filtering (FKF), and in the need of Inertial Navigation System/Global Positioning System/Tarrain Aided Navigation(INS/GPS/TAN) integration, an FKF method when the dimensions of subsystem vectors are different, and a local FKF filter structure are provided. With the utilization of many new information fusion techniques such as Local State Expansion, Local State Truncation, Intelligent Filtering In Irregular Cycle, Multi Level Fault Detection and Gating, the reliability and adaptability are apparently improved with a satisfying precision.