A Combination of Classification Robust Adaptive Kalman Filter with PPP-RTK to Improve Fault Detection for Integrity Monitoring of Autonomous Vehicles

A Combination of Classification Robust Adaptive Kalman Filter with PPP-RTK to Improve Fault Detection for Integrity Monitoring of Autonomous Vehicles

Real-time integrity monitoring (IM) is essential for autonomous vehicle positioning, requiring high availability and manageable computational load. This research proposes using precise point positioning real-time kinematic (PPP-RTK) as the positioning method, combined with an improved classification...

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Bibliographic Details
Main Authors: Hassan Elsayed, Ahmed El-Mowafy, Amir Allahvirdi-Zadeh, Kan Wang, Xiaolong Mi
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Remote Sensing
Subjects:
integrity monitoring
fault detection and identification
autonomous vehicles
PPP-RTK
robust estimation
adaptive Kalman filter
Online Access:https://www.mdpi.com/2072-4292/17/2/284
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Summary:Real-time integrity monitoring (IM) is essential for autonomous vehicle positioning, requiring high availability and manageable computational load. This research proposes using precise point positioning real-time kinematic (PPP-RTK) as the positioning method, combined with an improved classification adaptive Kalman filter (CAKF) for processing. PPP-RTK enhances IM availability by allowing undifferenced and uncombined observations, enabling individual observation exclusion during fault detection and exclusion (FDE). The CAKF reduces FDE computational load by using a robustness test instead of traditional FDE methods, improving precision and availability in protection level estimation. Epoch-wise weighting adjustments in the robustness test create a more accurate stochastic model, aided by an adaptive unit weight variance (UWV) calculated with a sliding window, achieving a 7–28% UWV reduction. Three test scenarios with up to four simultaneous faults in code and phase observations, ranging from 1 to 200 m and 0.4 to 20 m, respectively, demonstrated successful identification and de-weighting of faults, resulting in maximum positioning errors of 6 mm (horizontal) and 11 mm (vertical). The method reduced FDE computational load by 50–99.999% compared to other approaches.
ISSN:2072-4292

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