Real-time Detection and Parameter Estimation of UAV/Drone Signals using Adaptive Thresholds

The detection and parameter estimation of Unmanned Aerial Vehicle (UAV) and drone signals have been playing an essential role in passive surveillance systems since it provides inputs for other algorithms to localize, identify and naturalize hostile targets. In this paper, we propose an effective algorithm for reliably detecting UAV/Drone signals and accurately estimating their parameters using adaptive thresholds. The proposed scheme consists of several stages: adaptive noise floor estimation, detection statistics calculation, rising and falling edge detection, Time Difference of Arrival (TDOA) estimation and amplitude estimation. The proposed algorithm is designed in a sequential manner so that it can be implemented in hardware platforms without any difficulty. The proposed solution has been applied to the localization of UAVs/Drones in 3D space using TDOA principles. The proposed method has been shown to outperform classical techniques in all detection, estimation and localization metrics.