A Machine Learning Approach for Multipath Characterization and Mitigation Using Chipshape Observations

Sean Quiterio, Sanjeev Gunawardena

Peer Reviewed

Abstract:	Multipath continues to be a significant error source in satellite navigation. Recent solutions with Neural Networks (NN) model the effects of multipath on the autocorrelation function. Chipshape correlation provides a detailed look into the spreading code transitions in the time domain. It is useful in applications such as Signal Quality Monitoring (SQM) and is much more sensitive to multipath than autocorrelation. This research proposes NN models that each predict pseudorange or carrier range errors due to multipath by monitoring the chipshape correlation output. The code range model makes predictions for a simulation with 50 MHz precorrelation bandwidth and one multipath ray 3 dB below the line-of-sight (LOS) and a noise floor 14 dB above the LOS with an average of ±0.82 meters error, the carrier range model makes predictions with an average of ±5.63e-4 meters error. These models were accurate at predicting range errors for static multipath, however, the code range model is sensitive to the motion profile of the multipath ray relative to the LOS source.
Published in:	Proceedings of the 2024 International Technical Meeting of The Institute of Navigation January 23 - 25, 2024 Hyatt Regency Long Beach Long Beach, California
Pages:	898 - 916
Cite this article:	Quiterio, Sean, Gunawardena, Sanjeev, "A Machine Learning Approach for Multipath Characterization and Mitigation Using Chipshape Observations," Proceedings of the 2024 International Technical Meeting of The Institute of Navigation, Long Beach, California, January 2024, pp. 898-916. https://doi.org/10.33012/2024.19547
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