# Towards Explainable Artificial Intelligence for GNSS Multipath LSTM Training Models
## 4 Experimental Results
However, the pattern is not distinct enough to draw definitive conclusions about the model's internal mechanisms. Similarly, the CNR1 and CNR2 relevance distributions (Figure 6c,d) exhibit scattered patterns of neuron importance, while the EL relevance distribution (Figure 6e) shows a slightly uniform spread of relevance across neurons. The limitation in explainability from these initial results led to the decision to conduct further analysis with abnormal cases. By introducing controlled anomalies into the input data, we aimed to observe more pronounced changes in relevance patterns, potentially revealing clearer relationships between neuron activations and specific signal characteristics. This approach proved more fruitful in understanding the model's internal decision-making process, as discussed in subsequent sections. The final experiment investigated the relationship between LRP scores and signal anomalies, revealing a promising approach for anomaly detection in GNSS signals. Three sets of experiments were conducted by introducing artificial anomalies into different time segments of the data: from sample 1700 to 2000, 1400 to 2000, and 1100 to 2000, progressively increasing the duration of anomalous data. Figure 6: Distribution of LRP relevance scores across 256 neurons for different GNSS features under normal conditions: (**a**) first frequency multipath error variation (MP1), (**b**) second frequency multipath error variation (MP2), (**c**) first frequency carrier-to-noise ratio (CNR1), (**d**) second frequency carrier-to-noise ratio (CNR2), and (**e**) satellite elevation angle (EL). The relevance scores represent each neuron’s contribution to the model’s prediction, calculated using the LRP-gamma algorithm with \(\gamma=2.5\). The results, presented in Tables 3-5, consistently demonstrate that LRP scores increase significantly when anomalies are present in the corresponding features. For instance, when anomalies were introduced to MP1 between samples 1700 and 2000, its LRP score increased by 7.34%, while other features showed either minimal increases or slight decreases. Similar patterns were observed for other features, with each showing the highest increase in LRP scores when anomalies were specifically introduced to that feature. To validate these findings visually, Figure 7 presents comparative plots of LRP scores for normal and anomalous conditions for each feature. The plots clearly show elevated LRP scores during anomalous periods, particularly evident in the time segments where artificial anomalies were introduced. For example, Figure 7a,b contrast the MP1 LRP scores under normal and anomalous conditions, showing a marked increase in relevance scores during the anomalous period. This relationship between anomalies and increased LRP scores suggests a novel approach to anomaly detection in GNSS signals. Rather than relying solely on direct signal analysis or traditional machine learning classifications, monitoring changes in LRP scores could provide an additional layer of anomaly detection capability. This finding is particularly significant as anomaly detection remains a challenging problem in artificial intelligence applications for GNSS systems, where distinguishing between normal signal variations and actual anomalies is crucial for reliable navigation and positioning services. The consistency of these results across different anomaly durations and features strengthens the validity of using LRP score variations as an indicator of signal anomalies. This approach could potentially be developed into a real-time monitoring system where sudden increases in LRP scores could trigger alerts for potential signal anomalies, providing a new tool for GNSS signal quality monitoring and integrity assessment.