一种状态约束下的抗差自适应联邦滤波算法  

A Robust Adaptive Federated Filter Algorithm with State Constraints

作　　者：冯抗洪 宋迎春[1] 崔先强[1] FENG Kanghong;SONG Yingchun;CUI Xianqiang(School of Geosciences and Info-Physics,Central South University,932 South-Lushan Road,Changsha 410083,China)

机构地区：[1]中南大学地球科学与信息物理学院,长沙410083

出　　处：《大地测量与地球动力学》2024年第2期138-143,共6页Journal of Geodesy and Geodynamics

基　　金：国家自然科学基金(42174040)。

摘　　要：充分利用先验约束信息可以提高多传感器组合导航的定位精度和可靠性。将状态约束下的卡尔曼滤波扩展到传统联邦滤波中,提出一种状态约束下的联邦滤波算法。当子传感器出现异常时,在状态约束下的联邦滤波基础上,采用Huber方法调整子滤波器观测噪声矩阵,同时在信息分配阶段引入自适应信息分配因子,实时调整子滤波器融合权重,得到一种状态约束下的抗差自适应联邦滤波算法,以进一步减少不准确的子滤波器估计对融合结果的影响。将该方法应用在捷联惯导、全球导航卫星系统和里程计的多传感器组合导航系统中。仿真实验表明,状态约束下的联邦滤波估计精度优于传统联邦滤波,状态约束下的抗差自适应联邦滤波能够进一步提高观测异常下的导航定位精度和可靠性。The positioning accuracy and reliability of multi-sensor integrated navigation can be improved by making full use of prior constraint information.We extend Kalman filtering under state constraints to traditional federated filtering and propose a federated filtering algorithm under state constraints.When the sub-sensor is abnormal,we use Huber method to adjust the observed noise matrix of the sub-filter based on federated filtering under state constraints.Meanwhile,we introduce adaptive information sharing factor in the information sharing stage to dynamically adjust the fusion weight of the sub-filter and obtain a robust and adaptive federated filtering algorithm with state constraints,which further reduces the impact of inaccurate sub-filter estimates on the fusion results.The method is applied to the multi-sensor integrated navigation system of strapdown inertial navigation system,GNSS and odometer.The simulation results show that the estimation accuracy of federated filter under state constraints is better than that of traditional federated filter,and robust adaptive federated filtering can further improve the accuracy and reliability of navigation and positioning under abnormal observations.

关 键 词：联邦滤波 状态约束 Huber方法 自适应信息分配因子 

分 类 号：P228[天文地球—大地测量学与测量工程]