基于自适应噪声方差的卫星定位故障检测法  

An adaptive noise variance based fault detection algorithm for GNSS positioning

作　　者：陈含智 孙蕊 邱明 毛继志[2] 胡浩亮 张立东 CHEN Hanzhi;SUN Rui;QIU Ming;MAO Jizhi;HU Haoliang;ZHANG Lidong(College of Civil Aviation,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China;Laboratory of ATM Avionics Technology,China Aeronautical Radio Electronics Research Institute,Shanghai 201109,China)

机构地区：[1]南京航空航天大学民航学院,南京211106 [2]中国航空无线电电子研究所民航空管航空电子技术实验室,上海201109

出　　处：《北京航空航天大学学报》2023年第2期406-421,共16页Journal of Beijing University of Aeronautics and Astronautics

基　　金：国家自然科学基金(42174025,41974033);工信部民用飞机专项科研项目(MJ-2020-S-03);江苏省“六大人才高峰”项目(KTHY-014);中央高校基本科研业务费专项资金(KFJJ20200703);江苏省自然科学基金(BK20211569)。

摘　　要：不同观测环境的实际观测噪声可能存在较大差别,因此,固定的噪声方差矩阵可能使基于卡尔曼滤波故障检测法的故障检测效果下降。对此,提出自适应噪声方差的卫星定位故障检测算法,利用滑动窗内的历史新息实时估计观测噪声方差矩阵,在此基础上构建故障检测量与识别量对故障进行检测与识别,利用不含故障的新息更新状态向量并解算出定位结果。实验结果表明:所提算法在静态模式下,可100%检测和识别的最小单阶跃故障为3 m,对持续时间为100 s、斜坡故障增长速率为0.2 m/s的斜坡故障识别比率为51.4%,可100%检测和识别的最小多故障为4 m;在动态模式下,可100%检测和识别的最小单阶跃故障为10 m,对持续时间为200 s、斜坡故障增长速率为0.2 m/s的斜坡故障识别比率为66.25%,可100%检测和识别的最小多故障为12 m。所提算法性能优于基于最小二乘和卡尔曼滤波的故障检测法。As actual observation noises vary in different environments,the fixed noise variance matrix may degrade the performance of the Kalman filter(KF)-based fault detection method.To deal with this issue,we proposed an adaptive noise variance-based fault detection algorithm.Its fault detection and identification statistics are generated based on the real-time observation noise variance matrix estimated from historical innovations with a sliding window.The innovation without faults will then be used to update the state vector for positioning solutions.Both static and dynamic modes have been tested in the experiment.In the static mode,the proposed algorithm can provide a 100%fault detection rate(FDR)and fault identification rate(FIR)of the minimum single-step error of 3 m,and the FIR for the 0.2 m/s ramp error of 100 s is 51.4%.In addition,it can provide a 100%FDR and FIR of the minimum multiple error of 4 m.In the dynamic mode,the suggested algorithm can deliver a 100%FDR and FIR of the minimum single-step error of 10 m,and a 66.25%FIR for the 0.2 m/s ramp error of 200 s.In addition,it can provide a 100%FDR and FIR of the minimum multiple-error of 12 m.Its performance is superior to the least square residual-based method and the KF-based fault detection method.

关 键 词：全球卫星导航系统 全球卫星导航系统质量控制 故障检测 卡尔曼滤波 自适应滤波 

分 类 号：V249.3[航空宇航科学与技术—飞行器设计]