Clustering-Assisted Observation Domain Optimization for GNSS Multi-Fault Detection and Mitigation
Date
2024-07-15
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
With the rise of autonomous and semi-autonomous vehicles, effective fault detection and mitigation (FDM) methods have become essential in meeting the integrity requirements for precise and reliable Global Navigation Satellite System (GNSS)-based positioning. In scenarios involving multiple faulty observations, the existing GNSS-only statistical FDM methods are ineffective or impractical due to either theoretical model limitations or high computational costs. Additionally, supervised learning-based FDM approaches introduced in recent years do not meet the existing and emerging industry requirements due to dependence on large amounts of diverse training data, the accuracy of the offline labeling process, or high computational complexity. In this dissertation, a novel GNSS multi-fault detection and mitigation method is developed that achieves a balance between computational complexity and performance. The proposed method incorporates an Expectation Maximization (EM) framework to jointly estimate an approximate maximum likelihood of states and latent model parameters in the presence of observation outliers, i.e., faults. However, the EM algorithm is known for its high computational complexity. To reduce the computational complexity of EM, an importance sampling step based on unsupervised clustering is introduced. As demonstrated by the results and analysis herein, the proposed method outperforms the existing Least-squares Residuals-based single-fault method, achieving an average improvement of up to 48% in positioning accuracy. Additionally, the computational complexity of the proposed method is an order of magnitude lower than the state-of-the-art Solution Separation method. The improved performance and the lower computational complexity of the proposed method make it a suitable candidate for integration into modern standalone real-time GNSS applications.
Description
Keywords
GNSS, fault detection and mitigation, integrity monitoring, unsupervised learning, expecation maximization, RAIM
Citation
Haque, F. (2024). Clustering-assisted observation domain optimization for GNSS multi-fault detection and mitigation (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.