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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/2/284 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832587584039026688 |
|---|---|
| author | Hassan Elsayed Ahmed El-Mowafy Amir Allahvirdi-Zadeh Kan Wang Xiaolong Mi |
| author_facet | Hassan Elsayed Ahmed El-Mowafy Amir Allahvirdi-Zadeh Kan Wang Xiaolong Mi |
| author_sort | Hassan Elsayed |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9491773079304b70a4c630c65dbeded4 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-9491773079304b70a4c630c65dbeded42025-01-24T13:48:00ZengMDPI AGRemote Sensing2072-42922025-01-0117228410.3390/rs17020284A Combination of Classification Robust Adaptive Kalman Filter with PPP-RTK to Improve Fault Detection for Integrity Monitoring of Autonomous VehiclesHassan Elsayed0Ahmed El-Mowafy1Amir Allahvirdi-Zadeh2Kan Wang3Xiaolong Mi4School of Earth and Planetary Sciences, Curtin University, Perth 6102, AustraliaSchool of Earth and Planetary Sciences, Curtin University, Perth 6102, AustraliaSchool of Earth and Planetary Sciences, Curtin University, Perth 6102, AustraliaNational Time Service Center, Chinese Academy of Sciences, Xi’an 710600, ChinaDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong KongReal-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.https://www.mdpi.com/2072-4292/17/2/284integrity monitoringfault detection and identificationautonomous vehiclesPPP-RTKrobust estimationadaptive Kalman filter |
| spellingShingle | Hassan Elsayed Ahmed El-Mowafy Amir Allahvirdi-Zadeh Kan Wang Xiaolong Mi A Combination of Classification Robust Adaptive Kalman Filter with PPP-RTK to Improve Fault Detection for Integrity Monitoring of Autonomous Vehicles Remote Sensing integrity monitoring fault detection and identification autonomous vehicles PPP-RTK robust estimation adaptive Kalman filter |
| title | A Combination of Classification Robust Adaptive Kalman Filter with PPP-RTK to Improve Fault Detection for Integrity Monitoring of Autonomous Vehicles |
| title_full | A Combination of Classification Robust Adaptive Kalman Filter with PPP-RTK to Improve Fault Detection for Integrity Monitoring of Autonomous Vehicles |
| title_fullStr | A Combination of Classification Robust Adaptive Kalman Filter with PPP-RTK to Improve Fault Detection for Integrity Monitoring of Autonomous Vehicles |
| title_full_unstemmed | A Combination of Classification Robust Adaptive Kalman Filter with PPP-RTK to Improve Fault Detection for Integrity Monitoring of Autonomous Vehicles |
| title_short | A Combination of Classification Robust Adaptive Kalman Filter with PPP-RTK to Improve Fault Detection for Integrity Monitoring of Autonomous Vehicles |
| title_sort | combination of classification robust adaptive kalman filter with ppp rtk to improve fault detection for integrity monitoring of autonomous vehicles |
| topic | integrity monitoring fault detection and identification autonomous vehicles PPP-RTK robust estimation adaptive Kalman filter |
| url | https://www.mdpi.com/2072-4292/17/2/284 |
| work_keys_str_mv | AT hassanelsayed acombinationofclassificationrobustadaptivekalmanfilterwithppprtktoimprovefaultdetectionforintegritymonitoringofautonomousvehicles AT ahmedelmowafy acombinationofclassificationrobustadaptivekalmanfilterwithppprtktoimprovefaultdetectionforintegritymonitoringofautonomousvehicles AT amirallahvirdizadeh acombinationofclassificationrobustadaptivekalmanfilterwithppprtktoimprovefaultdetectionforintegritymonitoringofautonomousvehicles AT kanwang acombinationofclassificationrobustadaptivekalmanfilterwithppprtktoimprovefaultdetectionforintegritymonitoringofautonomousvehicles AT xiaolongmi acombinationofclassificationrobustadaptivekalmanfilterwithppprtktoimprovefaultdetectionforintegritymonitoringofautonomousvehicles AT hassanelsayed combinationofclassificationrobustadaptivekalmanfilterwithppprtktoimprovefaultdetectionforintegritymonitoringofautonomousvehicles AT ahmedelmowafy combinationofclassificationrobustadaptivekalmanfilterwithppprtktoimprovefaultdetectionforintegritymonitoringofautonomousvehicles AT amirallahvirdizadeh combinationofclassificationrobustadaptivekalmanfilterwithppprtktoimprovefaultdetectionforintegritymonitoringofautonomousvehicles AT kanwang combinationofclassificationrobustadaptivekalmanfilterwithppprtktoimprovefaultdetectionforintegritymonitoringofautonomousvehicles AT xiaolongmi combinationofclassificationrobustadaptivekalmanfilterwithppprtktoimprovefaultdetectionforintegritymonitoringofautonomousvehicles |