# Towards Explainable Artificial Intelligence for GNSS Multipath LSTM Training Models
## 1 Introduction  
GNSS systems serve as a critical infrastructure for modern positioning, navigation, and timing services. As these systems become increasingly integrated into safety-critical applications such as autonomous vehicles, precision agriculture, and aviation, ensuring their reliability and accuracy becomes paramount. One of the most significant challenges in GNSS operation is multipath interference, where signals reach receivers through multiplepaths due to reflections from surrounding structures. This phenomenon can significantly degrade positioning accuracy and reliability, particularly in urban environments. Recent advances in artificial intelligence, particularly deep learning, have shown promising results in addressing GNSS multipath challenges. However, the adoption of deep learning models in safety-critical GNSS applications faces a crucial obstacle: the lack of interpretability. While these models can achieve high accuracy in multipath detection and mitigation, their black-box nature makes it difficult to understand and validate their decision-making processes. This limitation poses significant risks in applications where system failures could have severe consequences. The present study addresses three fundamental challenges in the application of deep learning to GNSS multipath detection:  
* How can we develop deep learning models that not only detect multipath effects accurately but also provide interpretable results? * How can we validate the decision-making process of these models in a way that builds trust in their predictions? * How can we leverage model interpretability to enhance anomaly detection in GNSS signals? To address these challenges, we propose a novel framework that combines RNNs using LSTM cells with LRP. Our approach not only achieves high accuracy in multipath detection but also provides transparent insights into the model's decision-making process. We demonstrate this framework using real-world GNSS data collected from a Trimble BD970 dual-frequency receiver, focusing on scenarios where multipath effects are particularly challenging to detect and interpret. Our key contributions include:  
* The development of an interpretable deep learning architecture specifically designed for GNSS multipath detection. * The adaptation and implementation of LRP techniques for analyzing temporal dependencies in GNSS signals. * The discovery of a correlation between LRP relevance scores and signal anomalies, leading to a new method for anomaly detection. * Comprehensive evaluation using real-world GNSS data, demonstrating both the accuracy and interpretability of our approach. The development of GNSS multipath detection and mitigation techniques has evolved significantly over the past decades. Early research focused primarily on traditional signal processing approaches. In 2013, [PERSON] et al. [1] proposed a nonlinear regression approach using Support Vector Machine (SVM) to estimate multipath effects based on satellite elevation and azimuth angles. Their work demonstrated the potential of machine learning in GNSS applications, achieving improved accuracy compared to conventional methods. A significant advancement came in 2018 when [PERSON] et al. [2] introduced a Convolutional Neural Network (CNN)-based method for multipath detection. Their approach could detect approximately 80% of multipath-affected data, marking a substantial improvement in detection capabilities. Building upon this progress, in 2020, [PERSON] [3] proposed an LSTM-based multipath effect model that achieved remarkable accuracy rates of around 97% in prediction tasks. Most recently, [PERSON] et al. [4] conducted a comprehensive review of the application of machine learning in the GNSS use case. [PERSON] et al. [5] investigated deep learning for GNSS spoofing detection. An attention-based method for the GNSS jamming detection was introduced in [6]. In parallel with developments in multipath detection, significant advances have been made in GNSS interference monitoring and classification. [PERSON] et al.