Analysis of Micro-Doppler Signatures of Small UAVs Based on Doppler Spectrum

Kang, Ki-Bong; Choi, Jae-Ho; Cho, Byung-Lae; Lee, Jung-Soo; Kim, Kyung‐Tae

doi:10.1109/taes.2021.3074208

Cited by 53 publications

(15 citation statements)

References 36 publications

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“…The lowfrequency bird MD is similar to the measured bird except for the rigid-body MD that is caused by the uncompensated translational motion (Fig. 23 in [23]).…”

Section: A Simulation Conditionsupporting

confidence: 61%

“…For the multiple targets, the position in the training space was randomly selected and the targets were randomly placed within the resolution cell (Table I). The MDI of the model looks very similar to that of the quadcopter in [23] (Fig. 9); due to the plate-shape of the blade, the blade flashes occur with a period that corresponds to the period of rotation (Fig.…”

Section: A Simulation Conditionmentioning

confidence: 73%

“…9); due to the plate-shape of the blade, the blade flashes occur with a period that corresponds to the period of rotation (Fig. 12b in [23]). The lowfrequency bird MD is similar to the measured bird except for the rigid-body MD that is caused by the uncompensated translational motion (Fig.…”

Section: A Simulation Conditionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Protocol to Use FMCW Radar and CNN to Distinguish Micro-Doppler Signatures of Multiple Drones and Birds

et al. 2022

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Classification of multiple drones and birds by comparing micro-Doppler (MD) signatures is a very difficult task because the MD signatures of multiple birds can contaminate the MD signature of drones, and because a single drone can yield a very similar MD signature to that of multiple drones. In this paper, assuming a real observation scenario, we propose three protocols and analyze their accuracy in classification of multiple drones and birds by using the frequency modulated continuous wave radar and a convolutional neural network classifier. In simulations using models of rotating blades and of flapping wings, the method that uses training data that include combinations of drone and bird achieved an accuracy ~ 100 % for the majority vote classification; this result demonstrates that ours is the most appropriate method to distinguish multiple drones from birds.

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“…The lowfrequency bird MD is similar to the measured bird except for the rigid-body MD that is caused by the uncompensated translational motion (Fig. 23 in [23]).…”

Section: A Simulation Conditionsupporting

confidence: 61%

Section: A Simulation Conditionmentioning

confidence: 73%

See 1 more Smart Citation

Efficient Protocol to Use FMCW Radar and CNN to Distinguish Micro-Doppler Signatures of Multiple Drones and Birds

et al. 2022

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“…(e-mail: ansayed@uwaterloo.ca, michael.riad@uwaterloo.ca, oramahi@uwaterloo.ca, gshaker@uwaterloo.ca) combined with Machine Learning (ML) algorithms [1]- [4]. Most drones generate micro-doppler signatures, so range doppler and micro-doppler images are widely used to classify radar targets [5]- [7].…”

Section: Introductionmentioning

confidence: 99%

UAV Classification using Neural Networks and CAD-generated Radar Datasets

Sayed¹,

Riad²,

Ramahi³

et al. 2022

Preprint

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<p>Using micro-doppler signatures is an effective way to classify different types of UAVs, as well as other airborne objects such as birds. To generate signatures for drones, radar measurements are needed; however, these measurements are limited to the types of available drones, the radar parameters, the targets’ range, and the environments in which these measurements are conducted. In this paper, a new method for generating signature datasets is introduced. The method uses full-wave electromagnetic simulation software. Using this method, radar drones’ datasets can be generated using different types, sizes, drone materials, radar parameters, detected range, targets speed, and rotor RPM for rotary drones. A 77 GHz modeled FMCW radar is used to create dataset for classification purposes. Finally, a Convolutional Neural Network (CNN) algorithm is used to classify five types of drones. Based on the results, the classification of the drones is found to exceed 97% accuracy. </p>

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“…This method has a significant error, is easily affected by subjective factors, and is not suitable for processing large amounts of data. Other methods, such as the sum of cadence velocity diagram (SCVD) [22] and chopping frequency analysis [23], can be applied to estimate the rotor rotation frequency. The SVD left singular vector analysis and the Doppler frequency spectrum bandwidth analysis can be rented for the Doppler bandwidth [24].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Rotor Drone Micro-Motion Parameter Estimation Method Based on CVMD and SVD

Yang

Liang

et al. 2022

Remote Sensing

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It is of great significance to detect drones in airspace due to the substantial increase and regrettable misuse in the consumer market. In this paper, we establish a micro-motion theoretical model of a drone and analyze the micro-Doppler signature of rotor targets and the flicker mechanisms of the multi-rotor targets. Hence, for the target recognition problem of multi-rotor drones, a multi-rotor target micro-Doppler parameter estimation method is proposed. Firstly, a signal frequency domain segmentation method is proposed based on the complex variational mode decomposition (CVMD) to separate the high-frequency part of the high-frequency flicker in the frequency domain. Secondly, for the signal after frequency domain segmentation, a flicker time domain position method based on singular value decomposition (SVD) is proposed. Finally, by integrating CVMD frequency domain segmentation and SVD time domain positioning, the reconstruction of multi-rotor target scintillation at different speeds is realized, and the micro-motion parameters of rotor blades are successfully estimated. The simulation results show that the method has high accuracy in estimating the micro-motion parameters of a multi-rotor, which makes up for the shortage of the traditional method in estimating the micro-motion parameters of the multi-rotor target.

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Analysis of Micro-Doppler Signatures of Small UAVs Based on Doppler Spectrum

Cited by 53 publications

References 36 publications

Efficient Protocol to Use FMCW Radar and CNN to Distinguish Micro-Doppler Signatures of Multiple Drones and Birds

Efficient Protocol to Use FMCW Radar and CNN to Distinguish Micro-Doppler Signatures of Multiple Drones and Birds

UAV Classification using Neural Networks and CAD-generated Radar Datasets

A Multi-Rotor Drone Micro-Motion Parameter Estimation Method Based on CVMD and SVD

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