<![CDATA[The proliferation of small unmanned aerial vehicles (UAVs) near airports poses increasing risks to airspace safety and infrastructure security. This paper presents Smart Airport Radar, a simulation-based framework for classifying aerial threats — including drones, decoys, and birds — using multimodal AI features. The system emulates dynamic swarming behaviors and extracts five key descriptors — mean speed, heading variability, jerk, thermal signature, and radar cross-section (RCS) — to train a multiclass Support Vector Machine (SVM) classifier. Comparative analysis with a traditional RCS-based rule method shows the SVM achieving a classification accuracy of 93.33%, far outperforming the baseline at 20.00%. Radar-style trajectory visualizations and class-specific precision, recall, and F1-scores confirm the model’s robustness and interpretability. Beyond sensing and classification, the framework incorporates a communication link performance evaluation, analyzing classification accuracy under varying Signal-to-Noise Ratio (SNR) levels. Results reveal that maintaining link quality above 15 dB SNR preserves near-optimal detection performance, bridging radar sensing with wireless communication reliability. With minimal computational overhead, high adaptability, and strong cross-domain relevance, the proposed system offers a robust, explainable, and deployable solution for real-time perimeter defense in modern airport security infrastructures.]]>
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