GNSS Integrity Monitoring with Modelling Temporal Correlated Measurement Noise and an Application to RTK
| dc.contributor.advisor | Gao, Yang | |
| dc.contributor.author | Gao, Yuting | |
| dc.contributor.committeemember | O'Keefe, Kyle | |
| dc.contributor.committeemember | EI-Sheimy, Naser | |
| dc.date | 2021-11 | |
| dc.date.accessioned | 2021-09-16T19:30:11Z | |
| dc.date.available | 2021-09-16T19:30:11Z | |
| dc.date.issued | 2021-09 | |
| dc.description.abstract | The reliability of a navigation system is important in support of applications from self-driving cars to smartphones, especially for safety-critical and liability-critical applications which require stringent reliability requirements. In recent years, there is an increasing demand for global navigation satellite system (GNSS) integrity monitoring of precise positioning. Due to the features of fast response, strong autonomy and low-cost, user level GNSS integrity monitoring is an effective way to assess the reliability of GNSS positioning systems. It makes use of the measurements redundancy to check the statistical consistency of measurements and provide timely alarms. GNSS measurement noises are typically assumed to be uncorrelated Gaussian white noises in KF for GNSS integrity monitoring. However, this assumption does not hold in practical applications because GNSS observations can be contaminated by hardware noise, multipath, and unmodeled errors, resulting in correlated noises. To tackle the above limitations, an algorithm of user level GNSS integrity monitoring is proposed, which considers temporally correlated measurement noise with an application to real-time kinematic (RTK) positioning. An approach based on colored Kalman filter (CKF) is presented which considers measurement time correlation by a first-order autoregressive model and rebuilds a new measurement model for the CKF. The state variance matrix obtained by the CKF can accurately reflect the realistic position error, while the estimate of the standard Kalman filter is found to be overly optimistic. Then, by considering colored noises in standard KF, an algorithm of enhanced fault detection and exclusion is developed and investigated. This study examined and analyzed the CKF-based fault detection test, a fault identification test, a minimum detectable bias (MDB), error distribution, and positioning results. The CKF-based FDE can obtain realistic statistical information to improve integrity monitoring reliability by reducing false alarm rates. A new method of calculating protection level in the position domain is developed, based on a linear KF by modeling measurement time-correlated colored noise that provides a more realistic protection level in the position domain. | en_US |
| dc.identifier.citation | Gao, Y. (2021). GNSS integrity monitoring with modelling temporal correlated measurement noise and an application to RTK (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/39207 | |
| dc.identifier.uri | http://hdl.handle.net/1880/113881 | |
| dc.language.iso | eng | en_US |
| dc.publisher.faculty | Schulich School of Engineering | en_US |
| dc.publisher.institution | University of Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | en_US |
| dc.subject.classification | Engineering--Aerospace | en_US |
| dc.subject.classification | Engineering--Industrial | en_US |
| dc.title | GNSS Integrity Monitoring with Modelling Temporal Correlated Measurement Noise and an Application to RTK | en_US |
| dc.type | doctoral thesis | en_US |
| thesis.degree.discipline | Engineering – Geomatics | en_US |
| thesis.degree.grantor | University of Calgary | en_US |
| thesis.degree.name | Doctor of Philosophy (PhD) | en_US |
| ucalgary.item.requestcopy | false | en_US |