Data-Driven, Redundancy-Free Fault-Tolerant Inertial Navigation System Based on Sensor-to-Sensor Identification

Inertial navigation systems (INS) use measurements from inertial sensors (accelerometers and gyroscopes) to estimate the location of a moving object. If one of the sensors used by the INS becomes faulty during the system operation, the estimated location becomes inaccurate, which can lead to damage to the system or the environment. Therefore, in safety-critical applications such as aircraft navigation, redundant sensors are used to detect and disconnect faulty sensors, which can be expensive. In this paper, we propose a data-driven, redundancy-free fault-tolerant inertial navigation system based on sensor-to-sensor identification. We identify sensor-to-sensor models between sensors from a single inertial measurement unit (IMU). These models, which are identified under healthy conditions of the INS system, are used along with a subset of sensor measurements to predict the response of the remaining sensor measurements. If a sensor becomes faulty, we replace its faulty measurement with a healthy virtual measurement obtained using the identified sensor-to-sensor model and the remaining sensor measurements. We apply the proposed approach to two experimental setups, namely, a rotating platform and a moving vehicle. The proposed approach can be used in commercial applications to ensure low-cost safe operation of the INS system.