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.
GNSS Positioni ng Accuracy Enhancement Based on Robust Statistical MM Estimation Theory for Ground Vehicles in Cha llenging Environments
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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.