## 5 XAI Analysis for GNSS Signal Disruption Classification
### Local Explanations Results--SHAP Technique
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & & \multicolumn{6}{c}{**(a)**} \\ \hline
**Feature** & **DT** & **RF** & **KNN** & **SVM** & **AdaBoost** & **Average** & **Rank** \\ \hline Normalized spectrum kurtosis & 1 & 1 & 1 & 1 & 2 & 1 & 1 \\ Ratio of the variance to the squared mean of the normalized spectrum & 6 & 2 & 4 & 3 & 1 & 2.6 & 3 \\ Normalized spectrum flatness & 2 & 4 & 3 & 4 & 4 & 2.83 & 3 \\ Ratio of the maximum peak to the second maximum peak of the normalized spectrum & 5 & 6 & 5 & 5 & 5 & 4.3 & 4 \\ Normalized spectrum bandwidth & 3 & 5 & 6 & 6 & 6 & 4.3 & 4 \\ Single-frequency energy aggregation & 4 & 3 & 2 & 2 & 3 & 2.3 & 2 \\ \hline \multicolumn{6}{c}{**(b)**} \\ \hline
**Feature** & **DT** & **RF** & **KNN** & **SVM** & **AdaBoost** & **Average** & **Rank** \\ \hline Mean & 10 & 10 & 3 & 2 & 12 & 7.4 & 7 \\ Median & 9 & 9 & 2 & 3 & 11 & 6.8 & 7 \\ Standard deviation & 5 & 5 & 8 & 12 & 4 & 6.8 & 7 \\ Mean absolute deviation & 8 & 12 & 1 & 1 & 10 & 6.4 & 6 \\ Root mean square error & 1 & 2 & 7 & 4 & 6 & 4 & 4 \\
25 th percentile & 9 & 6 & 6 & 6 & 1 & 5.6 & 6 \\
75 th percentile & 6 & 7 & 4 & 5 & 9 & 6.2 & 6 \\ Inter-percentile range & 3 & 4 & 5 & 7 & 8 & 5.4 & 5 \\ Skewness & 8 & 8 & 9 & 8 & 3 & 7.2 & 7 \\ Kurtosis & 2 & 3 & 11 & 9 & 2 & 5.4 & 5 \\ Entropy & 4 & 1 & 10 & 10 & 5 & 6 & 6 \\ Maximum-to-mean ratio & 7 & 11 & 12 & 11 & 7 & 9.6 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: (**a**) Relative frequency domain feature importance (feature ranking). (**b**) Relative time domain feature importance (feature ranking). summary plot for multiclass classification illustrates what the XAI model has learned from these features. While analyzing the frequency domain features, except for KNN, RF, and other ML algorithms, the prediction of the model is determined by more than three features. For a clean signal, spectral flatness contributes the most. In a multipath signal, all features contribute equally except the normalized spectrum bandwidth and the ratio of the maximum peak to the second maximum peak of the normalized spectrum. For a pulse signal, spectral kurtosis is the most significant contributor. The least important feature is identified as the normalized spectrum bandwidth for the spoofing category. The plot shows that the normalized spectrum bandwidth has a greater contribution compared to other features. In both the CWI and MCWI cases, all features contribute equally, except the normalized spectrum bandwidth. Table 3b shows the feature ranking based on the SHAP algorithm. The majority of features are ranked between fourth and tenth; therefore, there is no combined effect of picking the best-matched features after fine-tuning the ML models' hyperparameters (DT: maximum depth from 4-50, minimum leaf samples from 5-20; RF: maximum depth from 5-50, number of estimators from 50-150; KNN: no. of neighbours from 4-10, leaf size from 5-50; SVM: regularization parameters 1-10, radial basis function, linear kernel; AdaBoost: depth from 5-50). Selecting the dominating attributes collectively is unreliable for the time domain features since every machine learning model generates a distinct rating. Based on the order of attributes, the time domain feature importance plot is shown; we can see that the class CWI hardly uses the median, skewness, mean, max.-to-mean ratio, and mean absolute deviation. Additionally, the pulse and chirp classes use the same features. Kurtosis contributed significantly to the MCWI signal, which is why confusion arises between these classes relatively often. In order to separate the MP and clean signal types in a better way, new features need to be generated that are uniquely dedicated to distinguishing these classes. The normalized spectrum kurtosis has contributed significantly more, followed by single-frequency energy aggregation. For testing on all ML models, the ratio of the maximum peak to the second maximum peak of the normalized spectrum did not show much importance at all.