A Comparative Study of Sensor Fault Detection Approaches applied to an Autonomous Solarpowered Aircraft Developed by Elektra Solar, an official spinoff of the DLR Institute for Robotics and Mechatronics (DLRRMC), the Elektra 2 is a solarpowered autonomous aircraft designed to endure long distances as well as high altitudes. The main motivation of this work is to develop and compare three different IMU sensor fault detection (FD) approaches to be further applied to the Elektra 2 Aircraft in real flight experiments. Currently, the Elektra 2 Solar aircraft provides a simple limitchecking of certain measurements such as aircraft angular velocities and pitch, roll and yaw angles. The first proposed FD approach is based on decoupled lateral and longitudinal linear models of the aircraft in combination with the ATLMS technique. The second proposed FD approach is based on a welldefined kinematic model of the aircraft in combination with an extended Kalman filter. The third FD approach is a modelfree approach based on principal component analysis. Simulation methodology covered different flight scenarios with different additive faults (abrupt, incipient, extra noise) applied to roll and pitch angles and rates.

Fault Detection and Exclusion for Tightly Coupled GNSSINS System Considering Fault in State Prediction To ensure navigation integrity for safetycritical 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 xe2x80x9cfilter faultxe2x80x9d), 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 hypothesistest methods. Following a detection event, the newlydesigned 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.