# Towards Explainable Artificial Intelligence for GNSS Multipath LSTM Training Models
## 5 Discussion
### Comparative Analysis and Model Evaluation  
While our approach demonstrates effectiveness in GNSS multipath detection and interpretation, it is important to consider alternative approaches that could potentially offer different advantages. In the realm of deep learning architecture, several competing approaches deserve consideration. Gated Recurrent Units (GRU) represent a compelling alternative, potentially offering similar performance to our LSTM-based approach while requiring approximately 25% fewer parameters. This reduction in model complexity could prove particularly valuable for resource-constrained GNSS receivers.  
Transformer architecture presents another interesting alternative, particularly for their ability to capture long-range dependencies through self-attention mechanisms. While these models excel at parallel processing and might offer improved performance in certain scenarios, their computational requirements and complexity in implementation could pose challenges for real-time GNSS applications. Similarly, CNN-LSTM hybrid architectures could potentially enhance accuracy by 5-10% through their ability to capture both spatial and temporal features, though this comes at the cost of increased architectural complexity and computational overhead.  
In terms of explainability approaches, several alternatives to LRP warrant consideration. SHAP (SHapley Additive exPlanations) values offer model-agnostic interpretability, potentially providing more flexible explanations across different model architectures. However, their computational cost typically exceeds that of LRP, particularly for time-series data. Integrated Gradients present another approach to feature attribution, though they may struggle with the temporal dependencies inherent in GNSS signals. LIME could offer localized interpretability with different trade-offs, but its approximations might not capture the full complexity of multipath effects.  
Traditional signal processing methods remain relevant as a baseline for comparison, offering computational efficiency and well-understood behavior. While these methods lack the adaptability and learning capabilities of deep learning approaches, they provide important benchmarks for evaluating the actual improvements achieved through more complex models.  
The comparison between these approaches reveals several fundamental trade-offs in GNSS multipath detection. Model complexity must be balanced against interpretability, with more sophisticated architectures potentially offering improved performance but at the cost of more challenging explanations. Computational efficiency trades against prediction accuracy, particularly relevant for real-time applications in GNSS receivers. The choice between different explainability methods involves balancing the quality of explanations against processing speed and resource requirements.  
These comparisons highlight both the strengths and limitations of our approach. While our LSTM-based architecture with LRP provides a balanced solution for interpretable multipath detection, alternative approaches might offer advantages in specific scenarios or use cases. This understanding points toward potential future investigations, particularly in exploring hybrid approaches that combine the strengths of different architectures while maintaining interpretability.