Multiple Faults Isolation for Multiconstellation GNSS Positioning Through Incremental Expansion of Consistent Measurements

Abstract

Fast and accurate fault detection and isolation (FDI) for multiple faults is crucial for satellite navigation systems. However, conventional deletion-based greedy search methods suffer from swamping effects, i.e., wrongly excluding healthy measurements, which leads to degradation in positioning performance after executing the isolation. This study proposes an incrementally expanding algorithm to isolate multiple faulty measurements in the multiconstellation global navigation satellite system (GNSS) positioning. The proposed algorithm is designed to find the most consistent set by incrementally expanding the minimum basic set with fault-free assumption. In each iteration, the no-fault hypothesis testing is conducted on the ordered studentized and jackknife residuals, enabling the correction of the fault-free assumption made in constructing the minimum basic set. The isolation performance and its impacts on positioning accuracy are evaluated in a worldwide simulation. The proposed method shows a 26% reduction in the swamping event rate and a 75% reduction in the mean postisolation positioning error, compared to the deletion-based greedy search method. Through Monte Carlo simulations, the stability of the proposed method regarding the number of faults and the fault magnitude is demonstrated. An application to the real-world dataset with artificially injected bias is also studied, showing a reduced postisolation positioning error.