Classification of Targets Using Statistical Features from Range FFT of mmWave FMCW Radars

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VERIFICATION OF CALCULATION METHOD FOR DRONE MICRO-DOPPLER SIGNATURE ESTIMATION

Drones micro-Doppler signatures obtained by FMCW radars are an excellent procedure for malicious drone detection, identification and classification. There are a number of contributions dealing with recorded spectrograms with these micro-Doppler signatures, but very low number of them has analyzed possibility to calculate echo caused by drone moving parts. In this paper, starting from already existing mathematical apparatus, we presented such spectrograms as a function of changing drone moving parts characteristics: rotor number, blades number, blade length and rotor moving speed. This development is the part of a wider project intended to prevent malicious drone usage

Radar UAV and Bird Signature comparisons with Micro-Doppler

This chapter reviews the similarities and differences between micro Unmanned Aerial Vehicles (UAVs), also referred to as drones, and bird targets from the signals they present to radar sensors. With the increasing usage of UAV platforms in both military and civilian applications, the demand for the ability to sense drone locations and discriminate them from background clutter and non-drone targets is becoming a vital requirement. A comparable target in size, speed and Radar Cross Section (RCS) is a bird. These are present almost everywhere that radar systems have to operate and have been detected by radar since the early origin of radar engineering. Due to the similarity in radar signature birds can cause common misclassification between them and the priority drone targets which has been identified as a current key challenge in radar sensing. In this chapter radar bird and drone signature research is initially summarised, then a fundamental model that represents the key contributions from drone rotor blades is introduced and compared to real measurements. Laboratory measurements of quadcopter rotor blade signatures with across 4 linear polarisations are then investigated in order to evaluate the trend of Signal-to-Noise-Ratio (SNR) vs. aspect angle. Next bird signatures from two separate radar systems are shown and compared to drone targets also present in the captures which are of comparable size and RCS. The outputs of all research presented are then summarised in the concluding remarks

레이더 스펙트로그램을 사용한 컨볼루션 신경망 기반 무인항공기 분류

학위논문 (석사) -- 서울대학교 대학원 : 융합과학기술대학원 융합과학부(지능형융합시스템전공), 2021. 2. 곽노준.With the upsurge in using Unmanned Aerial Vehicles (UAVs) in various fields, identifying them in real-time is becoming an important issue. However, the identification of UAVs is difficult due to their characteristics such as Low altitude, Slow speed and Small radar cross-section (LSS). To identify UAVs with existing deterministic systems, the algorithm becomes more complex and requires large computations, making it unsuitable for real-time systems. Hence, we need a new approach to these threats. Deep learning models extract features from a large amount of data by themselves and have shown outstanding performance in various tasks. Using these advantages, deep learning-based UAV classification models using various sensors are being studied recently. In this paper, we propose a deep learning-based classification model that learns the micro-Doppler signatures (MDS) of targets represented on radar spectrogram images. To enable this, first, we recorded five LSS targets (three types of UAVs and two different types of human activities) with a frequency modulated continuous wave (FMCW) radar in various scenarios. Then, we converted signals into spectrograms in the form of images by Short-time Fourier transform (STFT). After the data refinement and augmentation, we made our own radar spectrogram dataset. Secondly, we analyzed characteristics of the radar spectrogram dataset using the ResNet-18 model and designed the lightweight ResNet-SP model for the real-time system. The results show that the proposed ResNet-SP has a training time of 242 seconds and an accuracy of 83.39%, which is superior to the ResNet-18 that takes 640 seconds for training with an accuracy of 79.88%.본 논문에서는, 레이더 스펙트로그램 상에 형성된 서로 다른 이동표적의 고유한 마이크로 도플러신호를 학습하는 딥러닝 기반 분류모델을 제안한다. 이를위해 우리는 다섯가지 소형 이동표적(무인항공기 3종과 사람행동 2종)을 선정하여 주파수변조 연속파레이더로 표적들의 다양한 움직임을 측정하고 측정한 신호에 단시간 푸리에 변환의 신호처리과정과 데이터 정제 및 증강의 전처리과정을 적용하여 자체 레이더 스펙트로그램 데이터셋을 생성한다. 이후 광학이미지 분류모델인 ResNet-18을 사용하여 레이더 스펙트로그램 데이터셋의 특성을 분석한다. 레이더신호를 광학이미지로 변형하는 과정에서의 정보왜곡 및 손실을 가정하여 세가지 레이더 신호형태에 따른 성능을 비교하고 최적의 데이터형태를 확인한다. 노이즈 시험 및 구조에 따른 성능변화를 통해 모델이 학습하는 주요한 데이터 특징과 이상적인 모델구조를 확인한다. 마지막으로 레이더 스펙트로그램 데이터셋 특성분석을 기반으로 추가적인 경량화 및 안정화 기법을 적용하여 실시간 시스템을 위한 ResNet-SP 모델을 설계하고 ResNet-18모델과의 성능비교를 통하여 연산속도 증가와 안정성 및 정확성 향상 등의 성능개선을 확인한다.Abstract . . . . . . . . . . . . . . i Contents . . . . . . . . . . . . . . ii List of Tables . . . . . . . . . . . . iv List of Figures . . . . . . . . . . . . v 1 Introduction . . . . . . . . . . . . . . . . . . . . 1 2 Related Works . . . . . . . . . . . . . . . . . . . 5 2.1 Micro Doppler Signature (MDS) . . . . . . . . 5 2.2 Classification of UAVs using MDS . . . . . . . 6 3 Dataset Generation . . . . . . . . . . . . . . . . . 9 3.1 Measurement . . . . . . . . . . . . . . . . . . 10 3.2 Pre-processing . . . . . . . . . . . . . . . . . . 12 4 Models . . . . . . . . . . . . . . . . . . . . . . . 21 4.1 ResNet-18 . . . . . . . . . . . . . . . . . . . 22 4.2 ResNet-SP . . . . . . . . . . . . . . . . . . . . 27 5 Experiment . . . . . . . . . . . . . . . . . . . . . 32 5.1 Experiment Result . . . . . . . . . . . . . . . 32 5.2 Training Details . . . . . . . . . . . . . . . . . 33 6 Conclusion . . . . . . . . . . . . . . . . . . . . . 34 Abstract (In Korean) . . . . . . . . . . . . . . . . . 38Maste

Review of radar classification and RCS characterisation techniques for small UAVs or drones

This review explores radar-based techniques currently utilised in the literature to monitor small unmanned aerial vehicle (UAV) or drones; several challenges have arisen due to their rapid emergence and commercialisation within the mass market. The potential security threats posed by these systems are collectively presented and the legal issues surrounding their successful integration are briefly outlined. Key difficulties involved in the identification and hence tracking of these `radar elusive' systems are discussed, along with how research efforts relating to drone detection, classification and radar cross section (RCS) characterisation are being directed in order to address this emerging challenge. Such methods are thoroughly analysed and critiqued; finally, an overall picture of the field in its current state is painted, alongside scope for future work over a broad spectrum

Development of a K-band FMCW flexible radar prototype for detection and classification of nano-drones

© Cranfield University 2020. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright ownerNano-drones of the size of an insect can be used to perform stealthy surveillance or to gather intelligence crucial to attack roles at a relatively short range and within enclosed spaces and buildings. Conventional radar systems have been optimised to detect and classify bigger targets and are not specifically designed to detect nano-targets of less than 5 cm in size. Hence, this project aims to develop a radar system to detect and classify an insect-like size drone that corresponds to a low RCS. This will exhibit challenges due to the nature of the weak echoed signal that will be masked by an uninterested target with a stronger echoed signal. To tackle this sort of problem, micro Doppler extraction is applied for better target detection. This type of target that consists of a bladed propeller will give rise to a significant micro-Doppler signature that will contribute to the discernment of the interested target. An ad-hoc S-band FMCW radar prototype using off-the-shelf components An ad-hoc S-band FMCW radar prototype using off-the-shelf components has been successfully delivered. This prototype act as a groundwork for the next research phase of design and development for a higher frequency. Then, with the strong foundation of the S-band demonstrator, a flexible K-band FMCW radar prototype has successfully delivered aiming to meet the research purpose. The radar prototype offers a wide range of flexibility for the user to select the radar parameters (like operating frequency, ramp duration, bandwidth and integration time) and configure its performance. It will collect the signatures of real targets (nano-drone model) so that their performance can be assessed on experimental data. The results demonstrated that a nano-drone, a small size of less than 5 cm can be detected with the radar prototype developed.Ph