Analyzing Radar Cross Section Signatures of Diverse Drone Models at mmWave Frequencies | IEEE Journals & Magazine | IEEE Xplore

Analyzing Radar Cross Section Signatures of Diverse Drone Models at mmWave Frequencies | IEEE Journals & Magazine | IEEE Xplore

 R. Palamà, F. Fioranelli, M. Ritchie, M. R. Inggs, S. Lewis and H. Griffiths, "Measurements of Multistatic X&L Band Radar Signatures of UAVS," 2019 International Radar Conference (RADAR), Toulon, France, 2019, pp. 1-5.
doi: 10.1109/RADAR41533.2019.171389
Abstract: This paper illustrates the results of a series of measurements of multistatic radar signatures of small UAVs at L and X band. The system employed was the multistatic multiband radar system, NeXtRAD, consisting of one monostatic transmitter-receiver and two bistatic receivers. Results demonstrate the capability of the system of recording bistatic data with baselines and two-way bistatic range of the order of few kilometres.
keywords: {Radar cross-sections;Drones;Radar tracking;Transceivers;Conferences;multistatic radar;micro-Doppler;UAVs radar signatures},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9079112&isnumber=9078891

S. Harman, "Characteristics of the Radar signature of multi-rotor UAVs," 2016 European Radar Conference (EuRAD), London, UK, 2016, pp. 93-96.
Abstract: This paper investigates the theoretical Radar Cross Section of multi-rotor Unmanned Air Vehicles (UAVs), their time response and Doppler frequency signature. Anechoic chamber measurements of Multi-Rotor blades are presented and these measurements are used to give a more representative signature. A new radar for detection and discrimination of the Multi-Rotor aircraft is described. Results from radar detections of dynamic Multi-rotor UAVs in flight are then presented.
keywords: {Blades;Radar cross-sections;Aircraft;Radar detection;Doppler radar;Frequency measurement;Radar cross-sections;Radar detection;Unmanned aerial vehicles;Doppler radar},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7811655&isnumber=7811617

M. Ummenhofer, L. C. Lavau, D. Cristallini and D. O'Hagan, "UAV Micro-Doppler Signature Analysis Using DVB-S Based Passive Radar," 2020 IEEE International Radar Conference (RADAR), Washington, DC, USA, 2020, pp. 1007-1012.
doi: 10.1109/RADAR42522.2020.9114747
Abstract: Drones and unmanned aerial vehicles (UAVs) are increasingly popular, thus posing danger and threats to infrastructures and public safety. A technology for drone detection and classification would therefore significantly increase the level of security. In scenarios such as concerts, sport events, trade fairs, or in any situation where significant aggregation of people is present, such techniques should be non-invasive. That means they do not have to pose an additional threat to people themselves. To this end, passive radars offer an appealing solution, since they are able to offer a non-cooperative surveillance while not emitting any electromagnetic signal. On the contrary, they rely on existing transmitting infrastructure (also referred to as illuminators of opportunity, IoO), such as broadcasting signal sources (FM radio, terrestrial and satellite digital video broadcasting, cellular communication and so on). In this work, the possibility to exploit satellite television based passive radar for UAV detection is analyzed by experimental validation. In addition, micro-Doppler signatures for drones have been extracted, which might give information for subsequent UAV classification.
keywords: {Passive radar;TV;Spaceborne radar;Satellite broadcasting;Velocity control;Rotors;Autonomous aerial vehicles},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9114747&isnumber=9114538

M. Jian, Z. Lu and V. C. Chen, "Experimental study on radar micro-Doppler signatures of unmanned aerial vehicles," 2017 IEEE Radar Conference (RadarConf), Seattle, WA, USA, 2017, pp. 0854-0857.
doi: 10.1109/RADAR.2017.7944322
Abstract: In the paper, radar micro-Doppler signatures of rotating rotors are investigated for detection and identification of small UAVs. A 24 GHz dual-receiving channel interferometric radar is used to capture useful features of rotating rotors. Interferometric radar with two receiving channels can measure both radial velocity and angular velocity induced micro-Doppler modulations. The study found the angular micro-Doppler signature is a good complementary feature to the radial induced one for identifying small UAVs.
keywords: {Rotors;Doppler radar;Radar antennas;Angular velocity;Doppler effect;Blades;UAV detection;dual-receiver radar;interferometric radar;micro-Doppler effect;radial micro-Doppler;angular micro-Doppler},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7944322&isnumber=7944108

P. Beasley et al., "Multistatic Radar Measurements of UAVs at X-band and L-band," 2020 IEEE Radar Conference (RadarConf20), Florence, Italy, 2020, pp. 1-6.
doi: 10.1109/RadarConf2043947.2020.9266444
Abstract: This paper presents analysis of data captured with the NeXtRAD multistatic radar system during a fortnight of experimental trials in December 2019. The trials saw, for the first time, the NeXtRAD system capturing interleaved X-band and L-band measurements of multiple UAVs in simultaneous monostatic and bistatic configurations. Analysis is presented of the UAV's micro-Doppler signatures, permitting a discussion into the challenges some DAV platforms present for reliable detection. Comparisons are also made between X-band and L-band monostatic and bistatic UAV radar backscatter allowing conclusions to be drawn over the benefits of particular radar configurations for aiding UAV detection.
keywords: {Radar;Radar cross-sections;Doppler radar;Blades;Global Positioning System;Birds;Rotors;Radar;Bistatic Radar;Multistatic Radar;Micro Doppler;Drone;UAV;Doppler Signatures},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9266444&isnumber=9266311

A. K. Mitra, "Position-adaptive UAV radar for urban environments," 2003 Proceedings of the International Conference on Radar (IEEE Cat. No.03EX695), Adelaide, SA, Australia, 2003, pp. 303-308.
doi: 10.1109/RADAR.2003.1278757
Abstract: A bistatic radar concept is presented where a low-altitude UAV (unmanned aerial vehicle) "position-adaptively" converges to line-of-sight (LOS) locations for objects that are embedded between buildings. The concept is developed by deriving approximate electromagnetic signal models based on the uniform theory of diffraction (UTD). In addition, a new signature exploitation technique is formulated that allows for the estimation of target parameters in cases when neither the transmitting nor the receiving platform is in LOS with an embedded target or object. This technique is denoted as "exploitation of leakage signals via path trajectory diversity" (E-LS-PTD). Additional areas for further research are cited.
keywords: {Unmanned aerial vehicles;Electromagnetic modeling;Reflection;Signal analysis;Transmitters;Attenuation;Physical theory of diffraction;Radar applications;Electromagnetic propagation;Electromagnetic analysis},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1278757&isnumber=28560

J. A. Nanzer and V. C. Chen, "Microwave interferometric and Doppler radar measurements of a UAV," 2017 IEEE Radar Conference (RadarConf), Seattle, WA, USA, 2017, pp. 1628-1633.
doi: 10.1109/RADAR.2017.7944468
Abstract: The first dual-mode measurements of the time-varying radial and angular velocity signatures of a UAV quadcopter are presented. Measured with a compact 24 GHz interferometric radar, the signatures are measured at various observation angles relative to the UAV. It is shown that the signatures from the UAV at high grazing angles, when the radial velocity of the rotor blades relative to the radar is low, provide features related to the rotation rate of the rotor blades in certain instances. Using both Doppler and interferometric radar velocity measurements may therefore provide a method of detecting and classifying UAVs.
keywords: {Rotors;Doppler effect;Blades;Time-frequency analysis;Doppler radar;Velocity measurement},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7944468&isnumber=7944108

J. J. M. de Wit, R. I. A. Harmanny and G. Prémel-Cabic, "Micro-Doppler analysis of small UAVs," 2012 9th European Radar Conference, Amsterdam, Netherlands, 2012, pp. 210-213.
Abstract: Coherent radar measures micro-Doppler properties of moving objects. The micro-Doppler signature depends on parts of an object moving and rotating in addition to the main body motion (e.g. rotor blades) and is therefore characteristic for the type of object. In this study, the micro-Doppler signature (i.e. the object spectrogram) is exploited to classify small, unmanned helicopters and multicopters.
keywords: {Blades;Rotors;Spectrogram;Doppler effect;Doppler radar;Radar measurements;Micro-Doppler Signature;FMCW Radar;Mini UAVs},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6450745&isnumber=6450609

E. Vorobev, V. Veremyev and N. Tulenkov, "Experimental DVB-T2 Passive Radar Signatures of Small UAVs," 2019 Signal Processing Symposium (SPSympo), Krakow, Poland, 2019, pp. 67-70.
doi: 10.1109/SPS.2019.8881955
Abstract: The rapid development of small UAVs poses a serious threat to the protection of critical infrastructure and strategic objects as well as for private security. Passive radars are promising candidates for the detection of the UAVs to counteracting the possible threat. This paper presents the results of experimental investigations of the Doppler signatures of different small UAVs observed by passive radar exploiting DVB-T2 illuminators of opportunity. The experimental results revealed a presence of the distinctive features in Doppler signatures that can be used for classification between different types of UAV and other non-UAV targets.
keywords: {Blades;Passive radar;Doppler effect;Carbon;Radar detection;Unmanned aerial vehicles;passive radar;UAV;micro-Doppler;DVB-T2},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8881955&isnumber=8881950

V. V. Reddy and S. Peter, "UAV micro-Doppler signature analysis using FMCW radar," 2021 IEEE Radar Conference (RadarConf21), Atlanta, GA, USA, 2021, pp. 1-6.
doi: 10.1109/RadarConf2147009.2021.9454978
Abstract: Besides the detection of targets and estimation of their parameters, micro-motions of various target parts, that give rise to micro-Doppler signatures, are extracted from Radar acquisitions for target identification. Frequency modulated continuous-wave (FMCW) radar is identified to have the potential to perform both the activities. Due to high rotation rate of the rotor blades in unmanned aerial vehicles (UAV), the micro-Doppler frequency variation is significantly high giving rise to aliasing effect. In this work, we first study the signal model of FMCW radar in the presence of UAV. A new approach is then presented for the study of high frequency micro-Doppler signatures. Simulation example and experimental data show the efficacy of the approach for micro-Doppler signature analysis.
keywords: {Frequency modulation;Conferences;Radar detection;Rotors;Estimation;Radar;Unmanned aerial vehicles;Micro-motions;micro-Doppler signature;FMCW Radar;UAV identification;Drone detection},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9454978&isnumber=9454969

X. Guo, C. S. Ng, E. de Jong and A. B. Smits, "Concept of Distributed Radar System for mini-UAV Detection in Dense Urban Environment," 2019 International Radar Conference (RADAR), Toulon, France, 2019, pp. 1-4.
doi: 10.1109/RADAR41533.2019.171221
Abstract: In recent years, with the increasing prevalence of mini-Unmanned Aerial Vehicles (mini-UAVs, also called drones), there is an impetus to detect and monitor them in dense urban environment. However, the conventional radar which is generally mounted on the rooftop may not be able to detect drones in the urban canyons (i.e., streets between buildings) because the radar line-of-sight is often blocked by the buildings. To solve the problem, a novel concept is proposed in this paper, which uses distributed low-cost Commercial-Off-The-Shelf (COTS) radars to detect the small drones and create continuous coverage in highly urban environment. Such low-cost distributed radars can be mounted on the facades of buildings or the street lamp posts to save the space and are able to detect and classify the drones against other targets, such as vehicles, bicycles, walking persons, and birds etc. that are very common in urban area.
keywords: {mini-UAV detection;urban environment;distributed low-cost COTS radar;micro-Doppler signature},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9079047&isnumber=9078891

D. B. Herr, T. J. Kramer, Z. Gannon and D. Tahmoush, "UAV Micro-Doppler Signature Analysis," 2020 IEEE Radar Conference (RadarConf20), Florence, Italy, 2020, pp. 1-6.
doi: 10.1109/RadarConf2043947.2020.9266401
Abstract: The radar phenomenology of UAVs interacts with the micro-Doppler signal processing in interesting and useful ways. This paper adjusts the micro-Doppler processing to establish more consistent UAV micro-Doppler signatures and improve the separability of relevant features. The outlined approach is developed in the context of UAV rotor analysis. The techniques are demonstrated in both simulated and experimentally measured results of UAV rotor blades to distinguish three different types of UAV.
keywords: {Blades;Rotors;Doppler radar;Spectrogram;Radar cross-sections;Time-frequency analysis;Fourier transforms;Micro-Doppler;UAV;HERM Line;Blade Flash},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9266401&isnumber=9266311

X. Guo, C. S. Ng, E. de Jong and A. B. Smits, "Micro-Doppler Based Mini-UAV Detection with Low-Cost Distributed Radar in Dense Urban Environment," 2019 16th European Radar Conference (EuRAD), Paris, France, 2019, pp. 189-192.
Abstract: In recent years, the usages of consumer-grade mini-Unmanned Aerial Vehicles (mini-UAV, also called drones) are drastically increased. To detect and monitor the drone in a highly urbanised environment, a distributed radar system consisting of a group of low-cost small radar sensors is under study. In this paper, we present a micro-Doppler based automatic drone detection/classification system for the low-cost distributed radar sensors to effectively discriminate drones from other types of targets that are common in the urban area, such as vehicles, bicycles and walking persons. It consists of a two-step processing. The first step uses the complex cadence velocity diagram to extract the target micro-Doppler features and yields preliminary classification results. The second step jointly considers the current and previous N successive time segments to give the final determination. The two-step drone classification technique is implemented in our low-cost distributed radar demonstrator and tested in different locations of real environments. Promising drone classification results are demonstrated.
keywords: {mini-UAV (drone) detection/classification;micro-Doppler signature;automatic drone classification system},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8904760&isnumber=8904469

S. Harman, "Analysis of the radar return of micro-UAVs in flight," 2017 IEEE Radar Conference (RadarConf), Seattle, WA, USA, 2017, pp. 1159-1164.
doi: 10.1109/RADAR.2017.7944379
Abstract: This paper presents an analysis of the radar signature of micro-UAVs whilst in flight when subjected to realistic environmental flight conditions. These highly dynamic signatures have been found to be significantly different from modelled signatures or those expected from the RCS when measured in a benign environment. The time varying radar returns of different micro-UAV targets, measured in different conditions, are presented and characterized. Also the varying characteristics of the micro-Doppler rotor returns are analyzed. The impact of results on radar systems design is discussed.
keywords: {Radar cross-sections;Decorrelation;Doppler radar;Doppler effect;Radar antennas;Time measurement;Radar cross-sections;Radar detection;Unmanned aerial vehicles;PSNR;Signal analysis},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7944379&isnumber=7944108

Z. Gannon and D. Tahmoush, "Measuring UAV Propeller Length using Micro-Doppler Signatures," 2020 IEEE International Radar Conference (RADAR), Washington, DC, USA, 2020, pp. 1019-1022.
doi: 10.1109/RADAR42522.2020.9114778
Abstract: The low radar cross sections of unmanned aerial vehicles (UAVs) pose challenges for their proper classification. Recent works have examined the micro-Doppler (MD) signatures of UAVs for model classification. Here, the physical blade lengths of rotary wings are extracted from the MD blade flash phenomena such that correlated UAV features, such as size and weight, may be deduced to narrow the scope of subsequent classification. The proposed blade length estimation of rotating propellers is applied to simulated and experimentally collected MD measurements to characterize the blade flash phenomena. Assorted UAV blade lengths are experimentally estimated with less than 1% error.
keywords: {Doppler shift;Propellers;Blades;Rotors;Length measurement;Autonomous aerial vehicles;Feature extraction},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9114778&isnumber=9114538

G. Diao, J. Huitong, H. Ni, Z. Liu, N. Gon and L. Yang, "Study on the Modeling Method of Sip Target for Uav-borne Bi-SAR," 2020 3rd International Conference on Unmanned Systems (ICUS), Harbin, China, 2020, pp. 903-907.
doi: 10.1109/ICUS50048.2020.9274840
Abstract: To solve the simulation problem of uav-borne bi-static synthetic aperture radar (SAR) for sea surface targets, a scattering signatures modeling method of sea-surface target for bi-static imaging radar is proposed, based on the coupling scattering mechanism between the ship and sea surface. A bistatic scattering signature signal model for sea-surface target in conjunction with high frequency asymptotic techniques for electromagnetic (EM) scattering calculation, the multi-path EM scattering model, the time-evolving complex reflection coefficient model and the ship motion dynamics. The time varying scattering signatures for a typical ship on time evolving sea-surface are simulated and analyzed.
keywords: {Sea surface;Imaging;Radar imaging;Radar scattering;Reflection coefficient;Marine vehicles;Synthetic aperture radar;Uav-borne;Bi-static synthetic aperture radar;Sea-surface target;Scattering signature;Multi-path electromagnetic scattering effect},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9274840&isnumber=9274808

B. -S. Oh and Z. Lin, "Extraction of Global and Local Micro-Doppler Signature Features From FMCW Radar Returns for UAV Detection," in IEEE Transactions on Aerospace and Electronic Systems, vol. 57, no. 2, pp. 1351-1360, April 2021.
doi: 10.1109/TAES.2020.3034020
Abstract: In this article, an unmanned aerial vehicles detection system is proposed for an X-band ground-based surveillance frequency modulated continuous wave Doppler radar. Two novel features are directly extracted from the Doppler processing results without a time-frequency analysis. Experimental results on measured radar echo signals show that our system consistently outperforms the state of the art in terms of detection accuracy and computational efficiency.
keywords: {Doppler radar;Doppler effect;Radar detection;Feature extraction;Surveillance;Blades;Frequency modulated continuous wave (FMCW) surveillance radar;micro-Doppler signature analysis;unmanned aerial vehicle (UAV);UAV detection},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9241238&isnumber=9399890

A. N. Sayed, M. M. Y. R. Riad, O. M. Ramahi and G. Shaker, "A Methodology for UAV Classification using Machine Learning and Full-Wave Electromagnetic Simulations," 2022 International Telecommunications Conference (ITC-Egypt), Alexandria, Egypt, 2022, pp. 1-2.
doi: 10.1109/ITC-Egypt55520.2022.9855753
Abstract: Using micro-doppler signatures is an effective way to classify different types of UAVs, as well as other targets like birds. To generate these datasets, researchers used to conduct campaigns for radar drones’ measurements. However, these measurements are limited to the types of available drones, the used radar parameters, the targets’ range, and the environment these measurements are taken in. In this paper, a new method for simulating these types of datasets is introduced, this new method uses full-wave electromagnetic CAD tools. Radar simulations of five different types of real drones are presented. Using this method, researchers can simulate radar drones’ datasets using different types, sizes, and design materials of drones, they also can change the used radar parameters, detected range, targets speed, and rotors RPM for rotary drones. A 77 GHz FMCW simulated radar is used to generate the required dataset for classification purposes. Finally, a CNN algorithm is used to classify the five types of simulated drones, the accuracy of the used algorithm is better than 97%.
keywords: {Solid modeling;Machine learning algorithms;Radar measurements;Radar detection;Rotors;Radar;Classification algorithms;CNN;Datasets creation;Micro-Doppler signatures;Machine Learning;Range Doppler images;UAV classification},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9855753&isnumber=9855667

P. Molchanov, K. Egiazarian, J. Astola, R. I. A. Harmanny and J. J. M. de Wit, "Classification of small UAVs and birds by micro-Doppler signatures," 2013 European Radar Conference, Nuremberg, Germany, 2013, pp. 172-175.
Abstract: The problem of unmanned aerial vehicles classification using continuous wave radar is considered in this paper. Classification features are extracted from micro-Doppler signature. Before the classification, the micro-Doppler signature is filtered and aligned to compensate the Doppler shift caused by the target's body motion. Eigenpairs extracted from the correlation matrix of the signature are used as informative features for classification. The proposed approach is verified on real radar measurements collected with 9.5 GHz radar. Planes, quadrocopter, helicopters and stationary rotors as well as birds are considered for classification. Moreover, a possibility of distinguishing different number of rotors is considered. The obtained results show the effectiveness of the proposed approach. It provides capability of correct classification with a probability of around 95%.
keywords: {Feature extraction;Radar;Rotors;Noise;Doppler effect;Target tracking;Robustness},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6689141&isnumber=6689067

J. Mazumder and A. B. Raj, "Detection and Classification of UAV Using Propeller Doppler Profiles for Counter UAV Systems," 2020 5th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 2020, pp. 221-227.
doi: 10.1109/ICCES48766.2020.9138077
Abstract: The purpose of this paper is to develop an electronic system which detects and classifies an Unmanned Aerial Vehicle (UAV). For detection and classification of a target UAV, an X Band Continuous Wave (CW) Radar is designed. The corresponding micro-Doppler signatures were picked up and the signals are further processed in the system which gave various spectrographic patterns that enable us to detect and classify the UAV. To perform the signal processing on the received data, the MATLAB environment is used. Fast Fourier Transform (FFT) and short-time Fourier transform (STFT) are used to get Doppler information and frequency-time profile respectively, in this work. To test the system and check its performance, a quadcopter of multiple propeller blades was designed and used as a target. The entire set up was built in our radar Laboratory and experiments are conducted in the open-field environment. In this paper, significance of such UAV detections/classifications, development of a CW radar, radar signal acquisition/processing, extraction of main and micro Doppler signatures and using them for the UAV classifications are presented. In addition to classifications, the lengths of the propellers blades are also calculated using the extracted Doppler profile. All the experimental results related to these detections, classifications and measurements are reported and analysed.
keywords: {Radar cross-sections;Propellers;Radiation detectors;Blades;Radar detection;Autonomous aerial vehicles;Doppler radar;Micro-Doppler;Detection & Classification;STFT;Radar Signal Processing;Counter UAV System},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9138077&isnumber=9137848

I. Bouzayene, K. Mabrouk, A. Gharsallah and D. Kholodnyak, "Scan Radar Using an Uniform Rectangular Array for Drone Detection with Low RCS," 2019 IEEE 19th Mediterranean Microwave Symposium (MMS), Hammamet, Tunisia, 2019, pp. 1-4.
doi: 10.1109/MMS48040.2019.9157299
Abstract: Drones are becoming more and more available to the general public for leisure activities and exploited in commercial applications, this boom in drone use has contributed to the emergence of new threats in security applications. Because of their great agility and small size, UAV can be used for numerous missions and are very challenging to detect. Radar technology with its all-weather capability can play an important role in detecting UAV-based threats and in protecting critical assets, but standard radar is ill-prepared for UAV detection: UAVs are low-velocity aircraft with a very weak radar. A radar simulation is discussed and preliminary results are presented. In general, an X-band radar with electronic scanning capability can contribute to a reliable and affordable solution for detecting UAV threats. Radar could be the technology of choice for detecting the drone, signature.
keywords: {Drones;Radar cross-sections;Radar detection;Airborne radar;Doppler effect;Birds;Drone;UAV;radar detection;radar cross section;uniform rectangular array},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9157299&isnumber=9157247

P. Wellig et al., "Radar Systems and Challenges for C-UAV," 2018 19th International Radar Symposium (IRS), Bonn, Germany, 2018, pp. 1-8.
doi: 10.23919/IRS.2018.8448071
Abstract: Nowadays, unconventional Low Slow and Small (LSS) air threats pose serious challenges that cause deep concerns among military and civilian security organizations. Consequently, there is a high demand for robust and reliable counter small unmanned aerial vehicles (C-sUAV) solutions. However, traditional air defence systems may be unable to detect, identify and defeat some types of potentially hostile UAVs. Detection challenges such as small RCS values of air targets, unconventional flight patterns in low airspaces, terrain masking effects, or complex urban environments lead to high false alarm rates. Current C-sUAV systems in the market use improved radar components, originally either considered for VSHORAD (Very Short Air Defence) radar, battlefield radar, bird detection radar, perimeter surveillance radar, or high-resolution short-range radar. According to three NATO industrial advisory group (NIAG) studies [1]-[3], there is a strong need for improvement of the currently available C-sUAV systems and for the development of second generation robust and automated sense and warn systems. In addition, the evolution of advanced LSS air threats such as signature reduced drones or swarms as well as new scenarios [4] have to be considered in the development of the second generation. Therefore, many radar research activities on C-sUAV can be observed worldwide, for example research on passive radar, active multi-static radar, MIMO-radar, cognitive radar, or air-to-air radar. This article discusses current radar systems, challenges, and some radar research activities related to C-sUAV.
keywords: {Radar cross-sections;Radar detection;Drones;Radar antennas;Sensors;Birds},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8448071&isnumber=8447894

M. Ezuma, O. Ozdemir, C. K. Anjinappa, W. A. Gulzar and I. Guvenc, "Micro-UAV Detection with a Low-Grazing Angle Millimeter Wave Radar," 2019 IEEE Radio and Wireless Symposium (RWS), Orlando, FL, USA, 2019, pp. 1-4.
doi: 10.1109/RWS.2019.8714203
Abstract: Millimeter wave radars are popularly used in last-mile radar-based defense systems. Detection of low-altitude airborne target using these radars at low-grazing angles is an important problem in the field of electronic warfare, which becomes challenging due to the significant effects of clutters in the terrain. This paper provides both experimental and analytical investigation of micro unmanned aerial vehicle (UAV) detection in a rocky terrain using a low grazing angle, surface-sited 24 GHz dual polarized frequency modulated continuous wave (FMCW) radar. The radar backscatter signal from the UAV is polluted by land clutters which is modeled using a uniform Weibull distribution. A constant false alarm rate (CFAR) detector which employs adaptive thresholding is designed to detect the UAV in the rich clutter background. In order to further enhance the discrimination of the UAV from the clutter, the micro-Doppler signature of the rotating propellers and bulk trajectory of the UAV are extracted and plotted in the time-frequency domain.
keywords: {Clutter;Radar cross-sections;Radar detection;Propellers;Radar clutter;Doppler effect;CFAR;low-grazing angle;micro-UAV detection;mmWave radar;Weibull clutter},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8714203&isnumber=8714193

M. Farshchian, I. Selesnick and A. Parekh, "Bird body and wing-beat radar Doppler signature separation using sparse optimization," 2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa), Aachen, Germany, 2016, pp. 71-74.
doi: 10.1109/CoSeRa.2016.7745702
Abstract: Radar bird detection and discrimination has many civilian and non-civilian applications such as collision avoidance, false alarm reduction for detection radars, stealthy target detection, classification of military unmanned aerial vehicles (UAVs) and civilian drones, and conservation ecology. In order to develop new and improve existing detection and discrimination algorithms, this paper proposes a feature extraction technique in which the wing-beat Doppler radar signature of a bird is separated from its respective body signature. More specifically, we propose non-linear morphological component analysis (MCA) using invertible short-time Fourier transform (STFT) for feature extraction. The method is applied to the Peregrine falcon data measured by Alabaster et al. (2012) resulting in successful separation of the aforementioned signatures.
keywords: {Birds;Radar cross-sections;Doppler effect;Doppler radar;Radar remote sensing;Transforms},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7745702&isnumber=7745684

I. Hinostroza, T. Letertre and V. Mazières, "UAV detection with K band embedded FMCW radar," 2017 Mediterranean Microwave Symposium (MMS), Marseille, France, 2017, pp. 1-4.
doi: 10.1109/MMS.2017.8497143
Abstract: Electromagnetic simulations of a propeller, motor and arm of a commercially available UAV are presented. The goal was to identify the incidence angles of the wave where there is a strong variation of the radar cross section of the structure due to the position of the propeller, which will help in the Doppler analysis. Additionally measurements of the UAV were performed using a commercially available 24-GHz FMCW radar. Two scenarios were considered: hovering and vertical movement. In both cases the Doppler signature is characteristic.
keywords: {Propellers;Doppler effect;Radar cross-sections;Doppler radar;Bandwidth;octocopter;K band;FMCW radar;propeller;embedded},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8497143&isnumber=8497064