A Deep Unsupervised Learning Approach for GNSS Multipath Detection in High-Precision Air Transportation

With the rapid development of the low-altitude economy, the mitigation capability of global navigation satellite system (GNSS) multipath effects has emerged as a critical factor constraining the safety performance of aviation navigation. However, due to the coupled influence of multiple factors and inherent nonlinear characteristics, the precise detection of multipath effects and efficient classification of line-of-sight (LOS), multipath, and non-line-of-sight (NLOS) signals pose significant challenges. In recent years, machine learning methods have demonstrated notable advantages in multipath detection and achieved preliminary progress. Nevertheless, existing machine learning-based methods require extensive labeled multipath data. In practical applications, the high cost of data labeling and the dynamic variability of operational environments result in limited labeled data availability, rendering these methods inadequate for complex and evolving scenarios. Moreover, the extracted GNSS signal features exhibit insufficient inter-feature correlations and limited feature dimensions, severely limiting the further improvement of GNSS multipath detection and classification performance. In this paper, we propose a deep unsupervised learning for multipath detection (DUL-MD) method trained on unlabeled samples. This approach combines autoencoders (AE) and long short-term memory (LSTM) networks for joint representation learning, achieving low-dimensional mapping and enhancing time-series feature extraction capabilities. A novel loss function is introduced to better accommodate the data characteristics of GNSS multipath effects, including spatiotemporal correlation and overlapping boundaries. Extensive experiments based on static and dynamic vehicle and drone flight data demonstrate that the proposed deep unsupervised model achieves higher accuracy in detecting GNSS multipath effects compared to existing machine learning methods.