School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China.
Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.
Sensors (Basel). 2020 Jan 21;20(3):590. doi: 10.3390/s20030590.
To ensure navigation integrity for safety-critical applications, this paper proposes an efficient Fault Detection and Exclusion (FDE) scheme for tightly coupled navigation system of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS). Special emphasis is placed on the potential faults in the Kalman Filter state prediction step (defined as "filter fault"), which could be caused by the undetected faults occurring previously or the Inertial Measurement Unit (IMU) failures. The integration model is derived first to capture the features and impacts of GNSS faults and filter fault. To accommodate various fault conditions, two independent detectors, which are respectively designated for GNSS fault and filter fault, are rigorously established based on hypothesis-test methods. Following a detection event, the newly-designed exclusion function enables (a) identifying and removing the faulty measurements and (b) eliminating the effect of filter fault through filter recovery. Moreover, we also attempt to avoid wrong exclusion events by analyzing the underlying causes and optimizing the decision strategy for GNSS fault exclusion accordingly. The FDE scheme is validated through multiple simulations, where high efficiency and effectiveness have been achieved in various fault scenarios.
为确保安全关键应用的导航完整性，本文提出了一种高效的全球导航卫星系统（GNSS）和惯性导航系统（INS）紧耦合导航系统的故障检测与排除（FDE）方案。特别强调了卡尔曼滤波器状态预测步骤（定义为“滤波器故障”）中潜在故障的可能性，这些故障可能是由先前未检测到的故障或惯性测量单元（IMU）故障引起的。首先推导出积分模型，以捕获 GNSS 故障和滤波器故障的特征和影响。为适应各种故障情况，基于假设检验方法，分别为 GNSS 故障和滤波器故障严格建立了两个独立的检测器。在检测到故障事件后，新设计的排除功能可以（a）识别和去除有故障的测量值，（b）通过滤波器恢复消除滤波器故障的影响。此外，我们还通过分析潜在原因并相应地优化 GNSS 故障排除的决策策略，尝试避免错误的排除事件。FDE 方案通过多次模拟进行了验证，在各种故障场景中均实现了高效率和有效性。
