Implementation of solution separation-based Kalman filter integrity monitoring against all-source faults for multi-sensor integrated navigation

Accurate and safety-assured navigation is demanded by future autonomous systems such as automated vehicles and urban air mobility (UAM). These systems usually integrate multiple sensors to improve navigation accuracy and require the corresponding integrity monitoring architecture to ensure integrity. In response, we implement the Solution Separation-based Kalman filter integrity monitoring (SS-KFIM) technique to achieve fault detection and protection level evaluation for multi-sensor integrated navigation. In our implementation, the filter bank management strategies to handle sensor-in and sensor-out events are discussed. Besides, we consider the faults in state initialization and propagation phases aside from those at the measurement-update stage. Furthermore, our implementation can accommodate the cases where the all-in-view filter is not optimal in a least-squares sense. Simulations are conducted with an illustrative example where an inertial navigation system, global navigation satellite systems, and visual odometry are integrated for a UAM task. The results prove the high effectiveness and extended applicability of our implementation of the SS-KFIM algorithm.