A novel fault exclusion algorithm for Kalman filter-based RAIM with GPS and NavIC for aircraft navigation
Susmita Bhattacharyya · Measurement Science and Technology · 2025
Abstract In recent years, there has been growing interest in Kalman filter (KF)-based integrity monitoring. In this context, a novel fault exclusion (FE) algorithm is developed in this paper for aviation applications, extending prior work on receiver autonomous integrity monitoring for global navigation satellite systems. Schmidt KF (SKF) is used as a navigation filter to account for time-correlated measurement errors. The presented approach first reduces the computational load of existing work by suitably modifying the fault detection method with nominal changes in performance. Then, it designs an FE methodology, building on the fault detection tests with the SKF. Following the FE, the SKF is appropriately re-initialized, thereby precluding past faults from corrupting the current estimate. Re-initialization is achieved without implementing multiple filters, unlike existing strategies in the literature. Protection levels (PLs) are calculated to bound the position errors in the presence of undetected faults. The FE performance is studied in detail using global positioning system and navigation with Indian constellation signals simulated for an aircraft trajectory. Step, ramp and sinusoidal faults are introduced into multiple satellites at different time instants. The algorithm is shown to hold promise for successfully excluding faults. In only two of more than 50 failure scenarios, a 5 m step fault is not excluded, but the position errors remain bounded by the respective PLs. The new method also offers better performance than that of a conventional algorithm in the presence of ramp and sinusoidal faults. This is particularly important because position errors are unacceptable (i.e. oscillate or grow with time) as some of these faults are not excluded by the conventional approach, resulting in hazardous situations.