Neural Network-Based GNSS Code Measurement De-Weighting for Multipath Mitigation

Haoqing Li and Kyle O’Keefe

Peer Reviewed

Abstract: This paper proposes a multi-layer perceptron (MLP)-based neural network (NN) to estimate the variance of code measurements over time for multipath de-weighting purposes. Particularly, we aim to estimate the variance of a short window of code measurements using an MLP and use the output to de-weight measurements affected by multipath. The proposed solution can be widely used in various positioning techniques. The proposed method is trained and tested using a suburban land vehicle data set that includes segments of urban canyon environments. Positions are estimated using the standard Single Point Positioning (SPP) least-squares solution with a conventional residual test approach. Furthermore, the proposed method is compared with an expert model for variance estimation for multipath de-weighting and the standard elevation-dependent variance estimation model. We demonstrate that the MLP-based model can use NN to substitute human experience in the empirical variance model and provide a better positioning performance in both suburban and light-urban environments compared with the conventional code variance model and the empirical, or expert, variance model for multipath de-weighting.
Published in: Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024)
September 16 - 20, 2024
Hilton Baltimore Inner Harbor
Baltimore, Maryland
Pages: 3757 - 3768
Cite this article: Li, Haoqing, O’Keefe, Kyle, "Neural Network-Based GNSS Code Measurement De-Weighting for Multipath Mitigation," Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024), Baltimore, Maryland, September 2024, pp. 3757-3768. https://doi.org/10.33012/2024.19738
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