# Towards Explainable Artificial Intelligence for GNSS Multipath LSTM Training Models
## 6 Conclusions  
This research has demonstrated the successful application of Layer-wise Relevance Propagation for interpreting LSTM-based RNNs in GNSS multipath detection. The study makes several significant contributions to both GNSS signal processing and explainable artificial intelligence domains.  
First, we established an effective LSTM architecture capable of accurately predicting multiple GNSS parameters simultaneously, as evidenced by the minimal prediction errors across MP1, MP2, CNR1, CNR2, and EL features. The model's high accuracy provides a reliable foundation for subsequent interpretability analysis.  
Second, we successfully adapted the LRP-gamma technique for LSTM-based RNNs in GNSS applications, offering insights into the model's decision-making process. While initial analysis of normal conditions showed limited interpretability, the investigation of anomalous conditions revealed a crucial finding: LRP relevance scores consistently increase in the presence of signal anomalies. This discovery suggests a novel approach to anomaly detection in GNSS signals, where changes in LRP scores could serve as indicators of signal irregularities.  
The most significant contribution of this work lies in demonstrating that neural network interpretability can lead to practical applications beyond mere understanding. The correlation between LRP scores and signal anomalies provides a new tool for GNSS signal quality monitoring, combining the advantages of deep learning with explainable AI techniques.  
While our approach demonstrates promising results for GNSS multipath detection and analysis, several important limitations and constraints should be considered. For training data requirements, the quality of anomaly detection heavily depends on training data diversity. Collecting comprehensive GNSS data with verified multipath effects is challenging and time-consuming. For environmental constraints, performance of the proposed algorithm may vary significantly in different environmental conditions; especially for urban canyons, such complex multipath environments pose particular challenges. For implementation consideration, integration with existing GNSS receivers requires hardware modifications. The real-time performance of the DL-based method depends heavily on hardware capabilities. Furthermore, system reliability under continuous operation needs further testing.  
We have begun addressing some of these limitations in our ongoing research, particularly focusing on:  
* Developing more efficient LRP computation methods;
* Improving model generalization across different environments;
* Reducing computational requirements through optimization;
* Enhancing interpretation reliability through validation studies.  
These limitations do not invalidate the approach but rather define its scope and appropriate use cases. Understanding these constraints is crucial for successful practical implementation.  
Conceptualization, H.-S.W. and D.-J.J.; methodology, H.-S.W. and Z.-H.G.; software, Z.-H.G. and H.-S.W.; validation, H.-S.W. and D.-J.J.; writing--original draft preparation, H.-S.W.; writing--review and editing, H.-S.W. and D.-J.J.; supervision, H.-S.W. and D.-J.J. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the support of the National Science and Technology Council, Taiwan under grant number NSTC 113-2221-E-019-059.  
The data that support the findings of this study are available upon reasonable request from the authors.  
The authors declare no conflicts of interest.