## 5 XAI Analysis for GNSS Signal Disruption Classification
### Local Explanations Results--SHAP Technique
An illustration of a signal model prediction is displayed here, identifying the individual features' contributions and providing an error analysis of signal disruption. The base value is fixed as 0.142, and the predicted value comes closer by 0.12 for a pulse signal, where the standard deviation had higher values, and the rest of the features had much lower values. For chirp and MCWI, kurtosis is a common feature that pushed the model to a lower score. In the case of MP, all features pushed in the positive direction, but kurtosis played a dominant role in correct prediction. Conversely, for the spoofing signals, most of the features are located below the base value of 0.148, pushing the model toward a lower prediction value of 0.05. The force plot for chirp interference reveals that it significantly pushes the prediction higher with a contribution of +0.15, while kurtosis reduces it by \(-0.0235\), resulting in a final prediction of 0.1735. Similarly, in the MCWI case, kurtosis also contributed very little. In contrast,  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & & & \multicolumn{4}{c}{(**b**)} \\ \hline
**No. of Features** & **Metric** & **DT** & **RF** & **KNN** & **SVM** & **AdaBoost** \\ \hline \multirow{4}{*}{K = 5} & Accuracy & 0.7480 & 0.8393 & 0.7786 & 0.7750 & 0.6548 \\  & Precision & 0.7394 & 0.7692 & 0.7588 & 0.7740 & 0.5436 \\  & Recall & 0.7464 & 0.839  
most of the features help to predict the multipath instance, and none of the features are important for predicting the spoofing instance. Figure 10: _Cont._  
Figure 10: _Cont_. The pulse signal prediction is based on all strongly correlated features that are above the base value of 0.142, whereas for the CWI signal, only two features (single-frequency energy aggregation and normalized spectral kurtosis) lie above the base value of 0.1425. The clean and chirp signals are similar to each other, with the normalized spectral kurtosis  
Figure 10: (**a**) Waterfall summary plots (time domain), (**i**) Pulsed; (**ii**) CWI; (**iii**) MCWI; (**iv**) MP; (**v**) Clean; (**vi**) Chirp; (**vii**) Spoofing. (**b**) Waterfall summary plots (frequency domain), (**i**) Pulsed (**ii**) CWI; (**iii**) Clean; (**iv**) chirp; (**v**) MCWI; (**vi**) MP; (**vii**) Spoofing. pushing the model to a higher score. Most of the features contributed to MP prediction since all predicted values are above the base value to yield the correct occurrence. The One-vs-the-Rest (OvR) multiclass technique, commonly referred to as one-vs-all, is used to compute the area under the curve (AUC) for all machine learning models. To do this, an ROC curve must be computed for every GNSS signal class. In each iteration, one class is considered the positive class, and the other is regarded as the negative class. In the combined way, the classes are framed, and to consider every class equally, the metrics for each class are determined separately, and finally, we average them. By using these steps, the false positive and true positive rates of each class are aggregated based on the following equations:  
\[TPR=\sum_{i}\frac{TP_{i}  
The KNN performs best in detecting clean signals by plotting the ROC curve with a score of 96, whereas other ML models such as RF, SVM, and AdaBoost performs closely around 90 while the DT lags with 42. For CWI detection, RF shows superior performance with 83, the SVM provides marginal performance around 55, and AdaBoost is the lowest  
Figure 11: (**a**) SHAP force plots for GNSS time domain features, From top: (**i**) pulse, (**ii**) CWI, (**iii**) clean, (**iv**) chirp, (**v**) MCWI, (**vi**) multipath, and (**vii**) spoofing signals. (**b**) SHAP force plots for GNSS frequency domain features. From top: (**i**) pulse, (**ii**) CWI, (**iii**) clean, (**iv**) chirp, (**v**) MCWI, (**vi**) multipath, and (**vii**) spoofing signals. at 33.