Constructing Context-Aware GNSS Stochastic Model for Code-Based Resilient Positioning in Urban Environment

The global navigation satellite system (GNSS) positioning performance is widely recognized to degrade in challenging environments due to complex observation uncertainty and variability. In GNSS positioning framework, the stochastic model plays a crucial role in achieving unbiased parameter estimation by correctly capturing statistical observation characteristics. However, classical empirical stochastic models, such as elevation-dependent and carrier-to-noise ratio (C/N0)-based weighting schemes, are deemed inadequate for kinematic positioning services. The environmental context, which comprises terminal space and received signal characteristics, possesses the potential to promote quantifying observation uncertainty. To address this issue, we propose to construct context-aware adaptive GNSS stochastic models that integrate context information. Firstly, leveraging a substantial urban vehicle GNSS dataset, code residuals are accurately extracted using high-accuracy reference trajectory, with significant context features extracted and selected. Secondly, a temporal neural network (TNN) is developed for urban scenario recognition, including open sky, urban canyon, boulevard, and under viaduct. Additionally, context-aware stochastic functions are formulated through correlation analysis and function fitting between C/N0 and median code residuals. Finally, the context-aware stochastic model can be adaptively configured with context prediction results yield by TNN. Experimental results show the context detection model achieves an high-confidence accuracy of 95.37%. Furthermore, the proposed method is evaluated and compared with the classical weighting schemes using code-based single point positioning (SPP). Remarkably, the context-aware stochastic model surpasses the classical C/N0-based stochastic model in terms of continuity and accuracy, representing improvements of 17.60 and 22.39% for horizontal and vertical components, respectively, thereby highlighting its remarkable environment adaptability.