Energy-Based Radio Frequency Threshold Detection for Drone Sensing

This study investigates whether simple energy-based thresholding can function as an effective early-warning layer for drone detection using low-cost radio frequency hardware. A HackRF One software-defined radio was used to measure signal energy in the 2.4,GHz band during outdoor flights with a DJI Mavic 3T. A parameter sweep over the threshold multiplierkand smoothing window sizeNproduced an operating point ofk=2.9 andN=361 under a false-positive constraint of FP ≤ 0.5%, resulting in near-perfect detection across all test scenarios. Detections were obtained at station distances up to 800,m. When the controller and test station were co-located, energy levels decreased with increasing controller–drone separation, whereas circular-flight tests showed increasing energy with distance, suggesting adaptive transmission power control. These results demonstrate that energy-based thresholding can provide reliable early-warning capability in a multi-stage counter-UAS system and highlight how drone-controller geometry influences observed RF energy trends under real-world conditions.