Date of Award
3-2024
Document Type
Thesis
Degree Name
Master of Science in Computer Engineering
Department
Department of Electrical and Computer Engineering
First Advisor
Sanjeev Gunawardena, PhD
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 to predict errors in the Delay Lock Loop (DLL). 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. For a simulation with 50 MHz precorrelation bandwidth and an environment with one Line of Sight (LOS) source, one multipath ray with a Direct to Multipath ratio (D/M) of 3 dB, and noise with a Signal to Noise ratio (S/N) of -14 dB, the pseudorange model made predictions with an average of ±0.82 meters error, and the carrier range model made 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.
AFIT Designator
AFIT-ENG-MS-24-M-187
Recommended Citation
Quiterio, Sean A. L., "A Machine Learning Approach for Multipath Characterization and Mitigation Using Chipshape Observations" (2024). Theses and Dissertations. 7688.
https://scholar.afit.edu/etd/7688
Comments
A 12-month embargo was observed for posting this work on AFIT Scholar.
Distribution Statement A, Approved for Public Release. PA case number on file.