Localization and Activity Classification of Unmanned Aerial Vehicle Using mmWave FMCW Radars

Kumar, Prabhat; Idsøe, Henning; Yakkati, Rajesh Reddy; Kumar, Abhinav; Khan, Mohammed Zafar Ali; Yalavarthy, Phaneendra K.; Cenkeramaddi, Linga Reddy

doi:10.1109/jsen.2021.3075909

Cited by 56 publications

(25 citation statements)

References 14 publications

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“…The features also known as the Kim-Ling features [77] are base velocity, bandwidth of Doppler signal, offset, bandwidth without micro-Doppler, normalized standard deviation of Doppler signal strength, and cycle frequency. In [78], a drone localization and activity classification method is proposed using mmWave Frequency Modulated Continuous Wave (FMCW) radar. The drone's location is estimated from the range and elevation angle of arrival, whereas the micro-Doppler signatures are used to classify drone's activity by applying ML techniques (SVM, CNN and GBM), reportedly best results achieved using CNN.…”

Section: Radar-based Detectionmentioning

confidence: 99%

On the Detection of Unauthorized Drones—Techniques and Future Perspectives: A Review

et al. 2022

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The market size of civilian drones is tremendously increasing and is expected to reach 1.66 million by the end of 2023. The increase in number of civilian drones poses several privacy and security threats. To safeguard critical assets and infrastructure and to protect privacy of people from the illegitimate uses of commercial drones, a drone detection system is inevitable. In particular, there is a need for a drone detection system that is efficient, accurate, robust, costeffective and scalable. Recognizing the importance of the problem, several drone detection approaches have been proposed over time. However, none of these provides sufficient performance due to the inherited limitations of the underlying detection technology. More specifically, there are trade-offs among various performance metrics e.g., accuracy, detection range, and robustness against environmental conditions etc. This motivates an in-depth study and critical analysis of the existing approaches, highlighting their potential benefits and limitations. In this paper, we provide a rigorous overview of the existing drone detection techniques and a critical review of the state-of-the-art. Based on the review, we provide key insights on the future drone detection systems. We believe these insights will provide researchers and practicing engineers a holistic view to understand the broader context of the drone detection problem.

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Section: Radar-based Detectionmentioning

confidence: 99%

On the Detection of Unauthorized Drones—Techniques and Future Perspectives: A Review

et al. 2022

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“…From

, we must decide the range-and-angle (rangle) bins to track with the sensor. The tracking algorithm is implemented by first finding the rangle bin with a maximum energy at

[ 10 ]. Then, the rangle bin is updated for each new

after passing through a stability filter.…”

Section: Background Informationmentioning

confidence: 99%

Detecting the Presence of Intrusive Drilling in Secure Transport Containers Using Non-Contact Millimeter-Wave Radar

Wagner

Alkasimi

Pham

2022

Sensors

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We employ a 77–81 GHz frequency-modulated continuous-wave (FMCW) millimeter-wave radar to sense anomalous vibrations during vehicle transport at highway speeds for the first time. Secure metallic containers can be breached during transport by means of drilling into their sidewalls but detecting a drilling signature is difficult because the large vibrations of transport drown out the small vibrations of drilling. For the first time, we demonstrate that it is possible to use a non-contact millimeter-wave radar sensor to detect this micron-scale intrusive drilling while highway-speed vehicle movement shakes the container. With the millimeter-wave radar monitoring the microdoppler signature of the container’s vibrating walls, we create a novel signal-processing pipeline consisting of range–angle tracking, time–frequency analysis, horizontal stripe image convolution, and principal component analysis to create a robust and powerful detection statistic to alarm if drilling is present. To support this pipeline, we develop a statistical model combining the vibrating container and the random vibrations induced by vehicle movement to explore the robustness of the sensor’s detection capabilities. The presented results strongly support the inclusion of a millimeter-wave radar vibration sensor into a transport security system.

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“…ft is the transmitted modulated signal, fr is the received reflected signal, B is the signal modulation bandwidth, f0 is the initial frequency of the signal, fd is the Doppler shift, T is the modulation period of the signal, and τ is the time delay. For FMCW radar (triangular wave modulation mode), the range information and speed information between the radar and the target can be measured by the difference frequency signal Δ a f and Δ b f of the triangular wave for two consecutive cycles [23]. Then, the received signal after demodulation can be written as…”

Section: Data Augmentation Via Adjusting T-f Graph Display Scope and ...mentioning

confidence: 99%

“…In this paper, the classification of flying birds and rotary-wing drones is analyzed based on multi-feature fusion, i.e., the range profile, range-time/modulation periods and m-D features (T-F graph). Based on K-band frequency-modulated continuous wave (FMCW) radar [23,24], micro-motion signal measurement experiments were carried out for five different sizes rotary drones and flying birds, i.e., the SJRC S70 W, DJI Mavic Air 2, DJI Inspire 2, hexacopter, and single-propeller fixed-wing drone, and bionic and seagull birds. Based on the proposed data augmentation method, by setting the color display amplitude of the T-F graph and combining the range-time/periods features, the m-D dataset of flying birds and rotary wing drones was constructed.…”

Section: Introductionmentioning

confidence: 99%

Micro-Motion Classification of Flying Bird and Rotor Drones via Data Augmentation and Modified Multi-Scale CNN

et al. 2022

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Aiming at the difficult problem of the classification between flying bird and rotary-wing drone by radar, a micro-motion feature classification method is proposed in this paper. Using K-band frequency modulated continuous wave (FMCW) radar, data acquisition of five types of rotor drones (SJRC S70 W, DJI Mavic Air 2, DJI Inspire 2, hexacopter, and single-propeller fixed-wing drone) and flying birds is carried out under indoor and outdoor scenes. Then, the feature extraction and parameterization of the corresponding micro-Doppler (m-D) signal are performed using time-frequency (T-F) analysis. In order to increase the number of effective datasets and enhance m-D features, the data augmentation method is designed by setting the amplitude scope displayed in T-F graph and adopting feature fusion of the range-time (modulation periods) graph and T-F graph. A multi-scale convolutional neural network (CNN) is employed and modified, which can extract both the global and local information of the target’s m-D features and reduce the parameter calculation burden. Validation with the measured dataset of different targets using FMCW radar shows that the average correct classification accuracy of drones and flying birds for short and long range experiments of the proposed algorithm is 9.4% and 4.6% higher than the Alexnet- and VGG16-based CNN methods, respectively.

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Localization and Activity Classification of Unmanned Aerial Vehicle Using mmWave FMCW Radars

Cited by 56 publications

References 14 publications

On the Detection of Unauthorized Drones—Techniques and Future Perspectives: A Review

On the Detection of Unauthorized Drones—Techniques and Future Perspectives: A Review

Detecting the Presence of Intrusive Drilling in Secure Transport Containers Using Non-Contact Millimeter-Wave Radar

Micro-Motion Classification of Flying Bird and Rotor Drones via Data Augmentation and Modified Multi-Scale CNN

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