Fault diagnosis for MEMS INS using unscented Kalman filter enhanced by Gaussian process adaptation
Ivan Vitanov, Nabil Aouf · 2014
Miniature unmanned aerial vehicles (UAVs) such as quadrotors are increasingly in demand due to their small size and cost. The base navigation solution for such systems is typically a micro electro mechanical system (MEMS) based strap-down inertial navigation system (INS). To allow safe operation, navigation instrument failures need to be robustly handled through effective fault diagnosis. A popular approach to fault diagnosis in non-linear systems is the extended Kalman filter (EKF), which may, however, prove sub-optimal in the presence of greater non-linearity. In this paper, we instead adopt an unscented Kalman filter (UKF), which relies on a more accurate stochastic approximation - the unscented transform - rather than a Taylor series expansion. A downside to MEMS inertial navigation is an attendant time-dependent drift, which can distort estimation quality. Hence, MEMS INS sensors characteristically result in large biases in the navigation solution. To mitigate this problem we employ Gaussian Processes to approximate a time-dependent offset which can be utilised during on-line operation in an adaptive fashion, as a compensatory mechanism. We apply the enhanced GP-UKF by means of a bank of dedicated observers within an analytical redundancy framework. The results are competitive with the EKF and represent arguably the first application of an enhanced GP-UKF filter in the context of fault detection and isolation.