Detection of Micro-Doppler Signals of Drones Using Radar Systems with Different Radar Dwell Times

Author:

Gong JiangkunORCID,Yan Jun,Li Deren,Kong Deyong

Abstract

Not any radar dwell time of a drone radar is suitable for detecting micro-Doppler (or jet engine modulation, JEM) produced by the rotating blades in radar signals of drones. Theoretically, any X-band drone radar system should detect micro-Doppler of blades because of the micro-Doppler effect and partial resonance effect. Yet, we analyzed radar data detected by three radar systems with different radar dwell times but similar frequency and velocity resolution, including Radar−α, Radar−β, and Radar−γ with radar dwell times of 2.7 ms, 20 ms, and 89 ms, respectively. The results indicate that Radar−β is the best radar for detecting micro-Doppler (i.e., JEM signals) produced by the rotating blades of a quadrotor drone, DJI Phantom 4, because the detection probability of JEM signals is almost 100%, with approximately 2 peaks, whose magnitudes are similar to that of the body Doppler. In contrast, Radar−α can barely detect any micro-Doppler, and Radar−γ detects weak micro-Doppler signals, whose magnitude is only 10% of the body Doppler’s. Proper radar dwell time is the key to micro-Doppler detection. This research provides an idea for designing a cognitive micro-Doppler radar by changing radar dwell time for detecting and tracking micro-Doppler signals of drones.

Funder

Natural Science Foundation of Hubei Providence

Publisher

MDPI AG

Subject

Artificial Intelligence,Computer Science Applications,Aerospace Engineering,Information Systems,Control and Systems Engineering

Reference27 articles.

1. A review of copter drone detection using radar systems;Musa;Def. S&T Tech. Bull.,2019

2. Radar systems and challenges for C-UAV;Wellig;Proceedings of the 2018 19th International Radar Symposium (IRS),2018

3. Calibration of an X-band commercial radar and reflectivity measurements in suburban areas

4. DopplerNet: a convolutional neural network for recognising targets in real scenarios using a persistent range–Doppler radar

5. Radar Measurements for the Assessment of Features for Drone Characterization

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