Radar and Acoustic Sensor Fusion Using a Transformer Encoder for Robust Drone Detection and Classification
Conference
Ganganath, G, Thenahandi, PS, Kankanamge, SPK et al. (2026). Radar and Acoustic Sensor Fusion Using a Transformer Encoder for Robust Drone Detection and Classification
. Moratuwa Engineering Research Conference Mercon, 229-234. 10.1109/MERCon71835.2026.11691413
Ganganath, G, Thenahandi, PS, Kankanamge, SPK et al. (2026). Radar and Acoustic Sensor Fusion Using a Transformer Encoder for Robust Drone Detection and Classification
. Moratuwa Engineering Research Conference Mercon, 229-234. 10.1109/MERCon71835.2026.11691413
Unmanned aerial vehicles are increasingly deployed across diverse domains including security surveillance, precision agriculture, infrastructure inspection, and logistics. However, their rapid proliferation raises critical concerns regarding unauthorized intrusions, smuggling, and adversarial exploitation. Robust drone detection and classification systems are therefore essential. In this paper, we propose a multi-modal framework that fuses frequency-modulated continuous-wave radar and acoustic sensing using a transformer encoder-based architecture. Unlike single-modality approaches, our method exploits cross-modal complementarity to improve robustness against environmental noise, false alarms, and stealthy drone operations. Notably, our approach directly processes raw acoustic waveforms without domain transformations such as spectrograms, reducing preprocessing overhead and improving real-time applicability. Experimental results on our publicly released dataset demonstrate that the proposed method achieves excellent detection and classification accuracy under clean conditions, significantly outperforming existing state-of-the-art approaches while using fewer parameters.
