Design and Development of K-Band FMCW Radar for Nano-Drone Detection

Abstract

Nano-drones, are insect-like size drones with a capability of intrusion to provide intelligence and potentially violate secure establishments and public privacy rights. Nanodrones are already an existing technology which is becoming more and more available, portable, affordable and easily operated. As such, they may soon become a plausible defence and security threat. This paper presents the design and development of a K-Band FMCW radar prototype for nanodrone detection. The FMCW radar prototype consists of connectorised components operating at a carrier frequency of 24 GHz and offer high parameter selection flexibility. Experiments have been carried out in order to evaluate the system performance. Results show that a small Arcade PICO Drone Nano Quadcopter (smaller than 5 cm) could be detected, and that its micro-Doppler signature could be extracted from data.

Measurements of the Radar Cross Section of a nano-drone at K-band

“…Results show that the sphere returns are all within 1.5 dB and this also gives an indication of the expected accuracy of the measurement setup for such small targets at this range of frequencies. An Arcade Pico drone, whose micro-Doppler signatures were collected in [16], was used as the target under test for the RCS measurements. A photo of the Arcade Pico drone with an indication of the drone size is shown in Fig.…”

“…Measurements of a nano-UAV were conducted in [15] where a 35 GHz CW radar designed for 3D imaging was used to conduct a micro-Doppler analysis of a variety of UAVs, including mini, micro and nano-UAVs. Previous research aiming at designing and prototyping a flexible K-band FMCW radar to detect and collect micro-Doppler signatures of nano drones was presented in [16] and [17]. Results showed that the micro-Doppler signatures of an Arcade Pico-Drone could be successfully gathered in a laboratory environment, at a short range, with little transmitted average power.…”

Small Target Detection in a Radar Surveillance System Using Contractive Autoencoders

“…The used methods and scenarios show a large variety, e.g. passive radar [2], noise radar [3] or the detection of insect-like size nano-drones [4]. An overview of machine learning based methods for detection and classification can be found in [5], which also shows approaches from acoustics, optics and radio frequency identification.…”

DroneSense: The Identification, Segmentation, and Orientation Detection of Drones via Neural Networks

“…Despite the relative efficacy of acoustic and radio based systems the introduction of quiet micro-drones and fully autonomous drones (which do not require radio commands) has rendered them progressively less versatile and has necessitated the development of radar and optical based sensor systems. Radar in particular has seen extensive development including pulsed systems [40]- [42], Doppler systems [43]- [47], and Frequency Modulated Continuous Wave (FMCW) systems [48]- [50] all at multiple wavelengths [51]- [60]. The reader is directed to Refs [61]- [64] for a comprehensive review.…”