This paper presents a shape feature aided target detection method for micro-drone surveillance radar in order to mitigate the false alarms caused by the ground clutter. The method consists of a segmentation threshold selection method based on target measurements and a shape-feature extraction method based on Hu moments. Then the performance of the proposed method is verified experimentally using a real radar system. Field experiment using DJI phantom 3 is conducted, and the measured data is analysed. The results show that although there exist some limitations, the proposed method has good performance on eliminating the false alarms caused by the strong ground clutter in micro-drone detection and improving the target tracking accuracy.@en

The latest progress of the multiple-input multiple-output (MIMO) radar system developed for small drones detection at Beijing Institute of Technology is presented herein. A low-cost S-band MIMO scanning radar system is designed for the detection of small drones. A practical design optimisation and implementation of a sparse array covering the whole airspace, and a target-based MIMO radar array calibration method to improve the signal-to-noise ratio of the target are proposed. An experiment in the microwave chamber was conducted to verify the designed sparse-array beam-pattern, and measurement campaigns for small drones were performed to test the system performance where the radar achieved detection and tracking of a small DJI-Phantom 4 drone at a range of up to 5 km. Target features, including micro-Doppler and radar cross-section signatures were extracted to assist target classification. The experimental results indicate that the system has promising capability in small drones detection and tracking, despite the challenges of low dwell time on targets due to the scanning nature of the system.@en