基于双状态Χ~2检测和快速强跟踪AUKF的组合导航算法  

Integrated navigation algorithm based on two-state chi-square detection and fast strong tracking AUKF

作　　者：周朋进 吕志伟[1] 丛佃伟[1] 高扬骏 ZHOU Pengjin;LV Zhiwei;CONG Dianwei;GAO Yangjun(Information Engineering University,Zhengzhou 450000,China;Unit 66444 of PLA,Beijing 100042,China)

机构地区：[1]信息工程大学,郑州450000 [2]66444部队,北京100042

出　　处：《中国惯性技术学报》2019年第6期771-777,共7页Journal of Chinese Inertial Technology

基　　金：地理信息工程国家重点实验室开放研究基金项目(SKLGIE2015-M-2-5);国家自然科学基金(41604032)

摘　　要：针对GNSS/SINS/摄影测量组合导航中某个子系统发生故障时,整个导航系统易受到故障数据污染的问题,提出了一种基于快速强跟踪AUKF的双状态卡方(Χ^2)检测数据融合方法。首先,采用快速强跟踪AUKF算法进行滤波;然后,引入卡方检验通过检测UKF子滤波器输出的状态向量来定位故障参数;最后,采用强跟踪滤波准确跟踪状态矢量突变以增强系统鲁棒性,并根据自适应因子实时调整预测协方差阵以修正增益矩阵,使滤波结果不受异常信息的干扰。将提出的改进算法与常规算法分别应用于无人机着陆导航系统,结果显示:与传统UKF相比,提出的算法得到的位置误差减少了62.6%以上;与强跟踪UKF相比,导航误差也至少减小了32.6%。Aiming at the problem that the entire navigation system is vulnerable to pollution by fault data when there are subsystem faults in GNSS/SINS/photogrammetry integrated navigation, a two-state chi-square detection algorithm based on fast strong tracking AUKF(ST-AUKF) is proposed. Firstly, the fast ST-AUKF algorithm is used to perform filtering. Then, the chi-square test is introduced to detect the fault parameters by detecting the state vector outputs of the UKF sub-filter. Finally, the strong tracking filter is used to accurately track the state vector mutation, which enhances the robustness of the system. The prediction covariance matrix is adjusted in real time according to the adaptive factor to correct the gain matrix, so that the filtering result is not interfered by the abnormal information. Comparison tests are made between the proposed algorithm and the traditional UKF and ST-UKF algorithms by applying the three algorithms to the drone landing navigation system respectively, which show that the position errors by the proposed algorithm are reduced by more than 62.6% and 32.6% respectively compared with those of the UKF and the ST-UKF algorithms.

关 键 词：组合导航 快速强跟踪AUKF 状态Χ^2检测 信息融合 

分 类 号：TN965.5[电子电信—信号与信息处理]