رسالة جامعية
Sequential Importance Resampling Particle Filter for Ambiguity Resolution
| العنوان: | Sequential Importance Resampling Particle Filter for Ambiguity Resolution |
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| المؤلفون: | Manzano Islas, Roberto Rene |
| المساهمون: | O'Keefe, Kyle, El-Sheimy, Naser, Gao, Yang, Yang, Hongzhou, Bisnath, Sunil |
| بيانات النشر: | Arts University of Calgary |
| سنة النشر: | 2023 |
| المجموعة: | PRISM - University of Calgary Digital Repository |
| مصطلحات موضوعية: | GNSS ambiguity resolution, Particle filter, Non linear estimators, satellite navigation, satellite positioning, Statistics, Geodesy |
| الوصف: | In this thesis the sequential importance resampling particle filter for estimating the full geometry-based float solution state vector for Global Navigation Satellite System (GNSS) ambiguity resolution is implemented. The full geometry-based state vector, consisting on position, velocity, acceleration, and float ambiguities, is estimated using a particle filter in RTK mode. In contrast to utilizing multi-frequency and multi-constellation GNSS measurements, this study employed solely L1 GPS code and carrier phase observations. This approach simulates scenarios wherein the signal reception environment is suboptimal and only a restricted number of satellites are visible. However, it should be noted that the methodology outlined in this thesis can be expanded for cases involving multiple frequencies and constellations. The distribution of particles after the resampling step is used to compute an empirical covariance matrix Pk based on the incorporated observations at each epoch. This covariance matrix is then used to transform the distribution using the decorrelating Z transformation of the LAMBDA method [1]. The performance of a float solution based on point mass representation is compared to the typically used extended Kalman filter (EKF) for searching the integer ambiguities using the three common search methods described in [2]: Integer Rounding, Integer Bootstrapping, and Integer Least Squares with and without the Z transformation. As Bayesian estimators are able to include highly non-linear elements and accurately describe non-Gaussian posterior densities, the particle filter outperforms the EKF when a constraint leading to highly non-Gaussian distributions is added to the estimator. Such is the case of the map-aiding constraint, which integrates digital road maps with GPS observations to compute a more accurate position state. The comparison between the position accuracy of the particle filter solution with and without the map-aiding constraint to the solution estimated with the EKF is made. The algorithm is ... |
| نوع الوثيقة: | doctoral or postdoctoral thesis |
| وصف الملف: | application/pdf |
| اللغة: | English |
| العلاقة: | Manzano Islas, R. R. (2023). Sequential importance resampling particle filter for ambiguity resolution (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.caTest.; https://hdl.handle.net/1880/116932Test; https://dx.doi.org/10.11575/PRISM/41777Test |
| DOI: | 10.11575/PRISM/41777 |
| الإتاحة: | https://doi.org/10.11575/PRISM/41777Test https://hdl.handle.net/1880/116932Test |
| حقوق: | 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. |
| رقم الانضمام: | edsbas.FD54453E |
| قاعدة البيانات: | BASE |
| DOI: | 10.11575/PRISM/41777 |
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