采用双状态传播卡方检验和模糊自适应滤波的容错组合导航算法
Fault-tolerant integrated navigation algorithm using chi-square test with two state propagators and fuzzy adaptive filter

太阳能高空长航时无人机导航系统中, 捷联惯导/北斗2/全球卫星导航/星光导航(SINS/BD2/GPS/CNS)是一 种可用的组合方案. 针对常规容错组合导航算法故障检测类型单一, 故障时滤波精度下降的问题, 提出一种采用双 状态卡方检验(TSPCST)和模糊自适应滤波(FAF)的容错组合导航算法. 为了同时检测多种故障, 将TSPCST应用于 联邦滤波结构中; 为了防止故障数据污染系统, 利用FAF输出的高精度导航信息, 对双状态传播器定期交替校正; 进 一步, FAF运用TSPCST检测得到的故障信息变量, 定义量测子系统模糊有效域, 将检测阈值模糊化, 以弥补常规固 定检测阈值算法难以选取阈值的不足; 最后, 通过计算信息分配因子, 自适应处理多种故障数据. 仿真结果表明, 该 容错组合导航算法性能优于常规固定检测阈值算法.
SINS/BD2/GPS/CNS is a usable integrated scheme which is suitable for the navigation system of the highaltitude long-endurance solar-powered UAV. The normal fault-tolerant integrated navigation algorithm can only detect the single fault and the precision deteriorates in faulty operations. To solve these problems, a fault-tolerant integrated navigation algorithm is proposed using chi-square test with two state propagators (TSPCST) and the fuzzy adaptive filter (FAF). First, with the application of the TSPCST in the structure of the federal Kalman filter, the algorithm can detect multiple types of measurement faults at the same time. Then, these two state propagators are alternatively reset in a fixed period with the high precision navigation information provided by the FAF. By this method, the risk of using a contaminated data in the system is avoided. Furthermore, the FAF utilizes the fault context variables obtained by the TSPCST to define the fuzzy validity domains of each subsystem. By this method, the detection threshold is blurred to make up the drawback that it is difficult for the conventional algorithm with the fixed detection threshold to choose the threshold. Finally, the algorithm can process the failures adaptively through calculating the information distribution factor. Simulation results show that the performance of this algorithm is better than the conventional ones with the fixed detection threshold.