A Machine Learning Approach for GPS Code Phase Estimation in Multipath Environments

Mohamad Orabi, Joe Khalife, Ali A. Abdallah, Zaher M. Kassas, and Samer S. Saab

Peer Reviewed

Abstract:	A neural network (NN)-based delay-locked loop (DLL) for multipath mitigation in Global Positioning System (GPS) receivers is developed. The NN operates on equally-spaced samples of the autocorrelation function. The NN is trained using a statistical distribution model that takes into consideration multipath time delay and power attenuation. The performance of the proposed method is compared numerically and experimentally with three other conventional techniques: conventional early-minus-late DLL, narrow correlator, and high resolution correlator. It is demonstrated that the NN-based DLL produces smaller code phase root mean squared error compared to the three conventional techniques in high multipath environments.
Published in:	2020 IEEE/ION Position, Location and Navigation Symposium (PLANS) April 20 - 23, 2020 Hilton Portland Downtown Portland, Oregon
Pages:	1224 - 1229
Cite this article:	Orabi, Mohamad, Khalife, Joe, Abdallah, Ali A., Kassas, Zaher M., Saab, Samer S., "A Machine Learning Approach for GPS Code Phase Estimation in Multipath Environments," 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, Oregon, April 2020, pp. 1224-1229. https://doi.org/10.1109/PLANS46316.2020.9110155
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