Deep Learning-Based Diffraction Identification and Uncertainty-Aware Adaptive Weighting for GNSS Positioning in Occluded Environments

In natural canyons and urban occluded environments, signal anomalies induced by the satellite diffraction effect are a critical error source affecting the positioning accuracy of deformation monitoring. This paper proposes a deep learning-based method for diffraction signal identification and mitigation. The method utilizes a LSTM network to deeply mine the time-series characteristics of GNSS observation data. We systematically analyze the impact of azimuth, elevation, SNR, and multi-feature combinations on model recognition performance, demonstrating that single features suffer from incomplete information or poor discrimination. Experimental results show that the multi-dimensional feature scheme of “SNR + Elevation + Azimuth” effectively characterizes both signal strength and spatial geometric information, achieving complementary feature advantages. The overall recognition accuracy of the proposed method reaches 84.2%, with an accuracy of 88.0% for anomalous satellites that severely impact positioning precision. Furthermore, we propose an Adaptive Weighting Method for Diffraction Mitigation Based on Uncertainty Quantification. This method constructs a variance inflation model using the probability vector output from the LSTM Softmax layer and introduces Information Entropy to quantify prediction uncertainty, ensuring that the weighting model possesses protection capability when the model fails or is uncertain. In processing a set of GNSS data collected in a highly-occluded environment, the proposed method significantly outperforms traditional cut-off elevation and SNR mask strategies, improving the AFR to 99.9%, and enhancing the positioning accuracy in the horizontal and vertical directions by an average of 80.1% and 76.4%, respectively, thereby effectively boosting the positioning accuracy and reliability in occluded environments.