Machine learning ensemble models predict GNSS signal quality scores from indicators; activation functions convert scores to weights for WLS positioning, yielding error reductions and geographical transferability on Hong Kong and Tokyo urban datasets.
I nfluence of Satellite Geometry and Differential Correction on GPS Location Accuracy
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A Machine Learning Framework for Weighted Least Squares GNSS Positioning based on Activation Functions
Machine learning ensemble models predict GNSS signal quality scores from indicators; activation functions convert scores to weights for WLS positioning, yielding error reductions and geographical transferability on Hong Kong and Tokyo urban datasets.