Limited-Data Radar–Audio Fusion for Room-Level Intrusion Detection: Multi-Level Performance Evaluation and Cross-Environment Validation

Camera-free indoor security requires privacy-preserving sensing robust to disturbances and compatible with edge deployment. This study presents a limited-data radar–audio framework for room-level intrusion-related alarm detection. Data were collected using two colocated Infineon CY8CKIT-062S2-AI boards providing 60 GHz radar macro-detection outputs and stereo audio. Radar-only, audio-only, feature-level early-fusion, and score-level late-fusion branches were evaluated using session-disjoint training, validation, and test subsets, followed by stateful alarm processing. On the independent Room 1 test subset, validation-selected maximum-score late fusion achieved an F1-score of 0.897, compared with 0.842 for radar-only, 0.789 for audio-only, and 0.854 for early fusion. Stateful processing reduced false positives from 4.19 to 2.58 per 100 negative windows while retaining an F1-score of 0.888. Raspberry Pi 5 replay preserved all reference decisions. A live proof-of-concept sustained early-fusion and stateful maximum-score late-fusion processing at 48.53 ms and 46.50 ms per window, respectively, below the 0.8 s update interval. Without retraining or parameter adaptation, transfer to a second room retained F1-scores of 0.864 at window level and 0.859 after stateful processing, although false-positive behavior was more environment-sensitive. These results support score-level radar–audio fusion for limited-data edge-oriented indoor intrusion detection.