An Efficient Detector for Faulty GNSS Measurements Detection With Non-Gaussian Noises

TL;DR

This paper introduces the jackknife detector, a computationally efficient fault detection method for GNSS systems with non-Gaussian noise, demonstrating comparable accuracy to existing methods but with significantly improved speed.

Contribution

The paper presents the first rigorous, non-Gaussian noise-compatible fault detection method using the jackknife technique, with proven theoretical properties and real-world validation.

Findings

Achieves fourfold computational efficiency improvement over the SS detector.

Demonstrates equivalent detection performance to the SS detector in simulations.

Validates effectiveness in real-world satellite clock anomaly detection.

Abstract

Fault detection is crucial to ensure the reliability of navigation systems. However, mainstream fault detection methods are developed based on Gaussian assumptions on nominal errors, while current attempts at non-Gaussian fault detection are either heuristic or lack rigorous statistical properties. The performance and reliability of these methods are challenged in real-world applications. This paper proposes the jackknife detector, a fault detection method tailored for linearized pseudorange-based positioning systems under non-Gaussian nominal errors. Specifically, by leveraging the jackknife technique, a test statistic is derived as a linear combination of measurement errors, eliminating the need for restrictive distributional assumptions while maintaining computational efficiency. A hypothesis test with the Bonferroni correction is then constructed to detect potential faults in measurements. Theoretical analysis proves the equivalence between the jackknife detector and the solution separation (SS) detector, while revealing the former's superior computational efficiency. Through a worldwide simulation and a real-world satellite clock anomaly detection experiment--both involving non-Gaussian nominal errors--the proposed jackknife detector demonstrates equivalent detection performance to the SS detector but achieves a fourfold improvement in computational efficiency. These results highlight the jackknife detector's substantial potential for real-time applications requiring robust and efficient fault detection in non-Gaussian noise environments.