# Towards Explainable Artificial Intelligence for GNSS Multipath LSTM Training Models
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Features** & **Normal Value** & **MP1 Anomaly** & **MP2 Anomaly** & **CNR1 Anomaly** & **CNR2 Anomaly** & **EL Anomaly** \\ \hline MP1 & 0.475 & \({}^{*}\) 0.510 (7.34\%\(\uparrow\)) & 0.441 (7\%\(\downarrow\)) & 0.481 (1.33\%\(\uparrow\)) & 0.485 (2.2\%\(\uparrow\)) & 0.471 (0.78\%\(\downarrow\)) \\ \hline MP2 & 0.468 & 0.444 (4.98\%\(\downarrow\)) & \({}^{*}\) 0.500 (6.82\%\(\uparrow\)) & 0.455 (2.69\%\(\uparrow\)) & 0.489 (4.49\%\(\uparrow\)) & 0.427 (8.72\%\(\downarrow\)) \\ \hline CNR1 & 0.540 & 0.558 (3.25\%\(\uparrow\)) & 0.517 (4.27\%\(\downarrow\)) & \({}^{*}\) 0.673 (24.68\%\(\uparrow\)) & 0.544 (0.71\%\(\uparrow\)) & 0.556 (2.97\%\(\uparrow\)) \\ \hline CNR2 & 0.762 & 0.750 (1.53\%\(\downarrow\)) & 0.772 (1.32\%\(\uparrow\)) & 0.758 (0.56\%\(\downarrow\)) & \({}^{*}\) 0.826 (8.4\%\(\uparrow\)) & 0.792 (3.95\%\(\uparrow\)) \\ \hline EL & 0.822 & 0.823 (0.08\%\(\uparrow\)) & 0.824 (0.21\%\(\uparrow\)) & 0.816 (0.82\%\(\downarrow\)) & 0.829 (0.82\%\(\uparrow\)) & \({}^{*}\) 0.955 (16.11\%\(\uparrow\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of LRP relevance scores between normal data and data with anomalies injected from sample 1400 to 2000 for different features. Scores with asterisk (*) indicate the feature where anomaly was introduced, showing significant increase in relevance scores. The percentage in parentheses indicates the growth rate compared to normal conditions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Features** & **Normal Value** & **MP1 Anomaly** & **MP2 Anomaly** & **CNR1 Anomaly** & **CNR2 Anomaly** & **EL Anomaly** \\ \hline MP1 & 0.475 & \({}^{*}\) 0.510 (7.34\%\(\uparrow\)) & 0.432 (9.04\%\(\downarrow\)) & 0.484 (1.86\%\(\uparrow\)) & 0.492 (3.59\%\(\uparrow\)) & 0.471 (0.78\%\(\downarrow\)) \\ \hline MP2 & 0.468 & 0.444 (4.98\%\(\downarrow\)) & \({}^{*}\) 0.509 (8.81\%\(\uparrow\)) & 0.444 (5\%\(\downarrow\)) & 0.499 (6.7\%\(\uparrow\)) & 0.421 (8.72\%\(\downarrow\)) \\ \hline CNR1 & 0.540 & 0.558 (3.25\%\(\uparrow\)) & 0.509 (5.66\%\(\downarrow\)) & \({}^{*}\) 0.715 (32.48\%\(\uparrow\)) & 0.546 (1.18\%\(\uparrow\)) & 0.556 (2.97\%\(\uparrow\)) \\ \hline CNR2 & 0.762 & 0.750 (1.53\%\(\downarrow\)) & 0.775 (1.78\%\(\uparrow\)) & 0.756 (0.78\%\(\uparrow\)) & \({}^{*}\) 0.857 (12.5\%\(\uparrow\)) & 0.792 (3.95\%\(\uparrow\)) \\ \hline EL & 0.823 & 0.823 (0.08\%\(\uparrow\)) & 0.826 (0.48\%\(\uparrow\)) & 0.816 (0.78\%\(\uparrow\)) & 0.832 (1.1\%) & \({}^{*}\) 0.955 (16.1\%\(\uparrow\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of LRP relevance scores between normal data and data with anomalies injected from sample 1700 to 2000 for different features. Scores with asterisk (*) indicate the feature where an anomaly was introduced, showing significant increase in relevance scores. The percentage in parentheses indicates the growth rate compared to normal conditions. Figure 7: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Features** & **Normal Value** & **MP1 Anomaly** & **MP2 Anomaly** & **CNR1 Anomaly** & **CNR2 Anomaly** & **EL Anomaly** \\ \hline MP1 & 0.475 & * 0.510 (7.34\%) & 0.442 (6.79\%\(\downarrow\)) & 0.481 (1.33\%\(\uparrow\)) & 0.485 (2.2\%\(\uparrow\)) & 0.471 (0.78\%\(\downarrow\)) \\ \hline MP2 & 0.468 & 0.444 (4.98\%\(\downarrow\)) & * 0.499 (6.6\%\(\uparrow\)) & 0.455 (2.69\%\(\uparrow\)) & 0.489 (4.49\%\(\uparrow\)) & 0.427 (8.72\%\(\downarrow\)) \\ \hline CNR1 & 0.540 & 0.558 (3.25\%\(\uparrow\)) & 0.518 (4.14\%\(\downarrow\)) & * 0.673 (24.68\%\(\uparrow\)) & 0.544 (0.71\%\(\uparrow\)) & 0.556 (2.97\%\(\uparrow\)) \\ \hline CNR2 & 0.762 & 0.750 (1.53\%\(\downarrow\)) & 0.772 (1.28\%\(\downarrow\)) & 0.758 (0.56\%\(\downarrow\)) & * 0.826 (8.4\%\(\uparrow\)) & 0.792 (3.95\%\(\uparrow\)) \\ \hline EL & 0.822 & 0.823 (0.08\%\(\uparrow\)) & 0.824 (0.19\%\(\downarrow\)) & 0.816 (0.82\%\(\downarrow\)) & 0.829 (0.82\%\(\uparrow\)) & * 0.955 (16.11\%\(\uparrow\)) \\ \hline \hline \end{tabular}
\end{table}