Sequential Radar–Acoustic Drone Detection Using A Lightweight Deep Learning Framework for Edge-Based Sensor Networks

Distributed sensor networks (DSNs) with multimodal sensors deployed in dense urban environments require drone detection algorithms that are both reliable under poor signal conditions and feasible for execution on resource-constrained edge devices. Although multimodal sensing can improve detection robustness, most existing approaches rely on parallel processing and fusion strategies that incur significant computational overhead and complex inter-modality dependencies. This paper presents a sequential radar–acoustic drone detection framework tailored for low-complexity edge deployment. The proposed method employs radar as the primary detection stage and activates acoustic analysis only when radar confidence falls within a predefined uncertainty margin. Each modality is processed independently using a shallow convolutional neural network. Confidence thresholds derived from radar classifier statistics regulate the sequential decision process. Experimental results demonstrate that the proposed sequential pipeline preserves high specificity while reducing missed detections compared to radar-only processing. Quantitative evaluation shows that the sequential architecture reduces false negatives by up to 41