Exploring and Utilizing Multipath Effects on L5 for Multi-Frequency Machine Learning-Based Positioning

Nesreen I. Ziedan

Peer Reviewed

Abstract:	The modernized L5 GPS signal has enhanced structure and properties compared to the legacy L1 C/A signal. The length of the PRN code of the L5 signal is 10 times that of the L1 C/A signal and the chipping rate of the L5 signal is 10 times faster. One objective of the higher chipping rate and longer code length is to have a better multipath protection. Theoretically, multipath signals with extra path length, defined as Lm, longer than about 1.5 chips, or 45 m, would have a minimal or no effect on the tracking accuracy. However, in urban areas, multipath signals can have Lm ranging from a few meters to 200-300 m. The objective of this paper is to utilize the information that can be extracted from the received L5 signal in the design of a multipath mitigation algorithm. This is to enhance the positioning accuracy in urban environments. Therefore, the correlator coverage of the Cross-Ambiguity Function (CAF) of the L5 signal is extended to several chips to collect multipath information outside of the 45 m covered by 1.5 chipS. The characteristics of the multipath-distorted CAF are utilized in Deep Neural Network (DNN) classification and regression algorithms to classify the multipath signal and estimate the code delay error, respectively. The proposed classification DNN has 4 classes based on the presence of one or more separable CAF peaks and the distortion in the CAF shape. The outputs of the DNNs are used in a proposed 3D Mapping-Aided (3DMA) Optimized Position Estimation (OPE) for multifrequency (MF), or OPE-MF algorithm. The OPE-MF algorithm loosely integrates data from both the L1 and L5 signals in its functionality. The results show that the OPE-MF algorithm can have up to 90% and around 30% positioning accuracy enhancement compared to the positioning accuracies of a standard L5 navigation solution and an OPE algorithm that uses only L1 C/A data, respectively.
Published in:	Proceedings of the 38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025) September 8 - 12, 2025 Hilton Baltimore Inner Harbor Baltimore, Maryland
Pages:	2159 - 2172
Cite this article:	Ziedan, Nesreen I., "Exploring and Utilizing Multipath Effects on L5 for Multi-Frequency Machine Learning-Based Positioning," Proceedings of the 38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025), Baltimore, Maryland, September 2025, pp. 2159-2172. https://doi.org/10.33012/2025.20283
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