Modeling Small UAV Micro-Doppler Signature Using Millimeter-Wave FMCW Radar

Passafiume, Marco; Rojhani, Neda; Collodi, Giovanni; Cidronali, Alessandro

doi:10.3390/electronics10060747

Cited by 23 publications

(26 citation statements)

References 26 publications

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“…Passafiume et al [12] introduced a fully deterministic model for the micro-Doppler signatures of flying UAVs in frequency-modulated continuous-wave (FMCW) radar echo signals (6). Such a model considers the effects of mechanical vibrations on the range distance and thus introduces a varying term due to vibrations into the range argument alongside the Doppler effects on the radar signal.…”

Section: B Model-based Dataset Augmentermentioning

confidence: 99%

“…Contrary to a stationary object, objects in motion generate modulated Doppler components referred to as the micro-Doppler signature, which is provided as additional components of the Doppler signature of the drone's fuselage. Furthermore, micro-Doppler signatures rely on the number of motors, motor speed, and orientation of the drone; therefore, by analyzing the micro-Doppler signatures, information pertaining to the drone can be obtained [12], [13].…”

mentioning

confidence: 99%

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Model-Based Data Augmentation Applied to Deep Learning Networks for Classification of Micro-Doppler Signatures Using FMCW Radar

Rojhani

Passafiume

Sadeghibakhi

et al. 2023

IEEE Trans. Microwave Theory Techn.

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Deep neural networks (DNNs) have become a relevant subject in the classification of radio frequency signals and remote sensing data. A primary challenge is a tradeoff between obtaining data that are suitable for DNN training and the effort that making experimental measurements requires. Hence, the quality and quantity of data used for the training and testing of models are crucial for effective classifier development. The training dataset should cover a wide range of cases that synthesize the actual scenarios being classified. This work proposes a novel data augmentation method based on a deterministic model to generate a simulated dataset of radar micro-Doppler signatures suitable for unmanned aerial vehicle (UAV) target classification, without requiring measurement data. It is shown that the DNN trained using the properly generated model-based data offers improved classification accuracy performance. Resultsare presented for a two-class classification of the number of UAV motors using a 77-GHz frequency-modulated continuouswave (FMCW) automotive radar system. The effectiveness of the proposed methodology is proven: a classification accuracy of 78.68% is achieved using a convolutional neural network (CNN) trained using the synthetic dataset, while an accuracy of 66.18% is achieved by using a typical signal processing data augmentation method on a limited measured dataset.

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Section: B Model-based Dataset Augmentermentioning

confidence: 99%

mentioning

confidence: 99%

Model-Based Data Augmentation Applied to Deep Learning Networks for Classification of Micro-Doppler Signatures Using FMCW Radar

Rojhani

Passafiume

Sadeghibakhi

et al. 2023

IEEE Trans. Microwave Theory Techn.

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“…MTI is a popular clutter rejection filter. MTI can be used to discriminate moving aerial vehicles, e.g., UAVs from clutter [104]. In [104], [105] MTI is used for the detection and classification of UAVs.…”

Section: E Motion Filteringmentioning

confidence: 99%

“…MTI can be used to discriminate moving aerial vehicles, e.g., UAVs from clutter [104]. In [104], [105] MTI is used for the detection and classification of UAVs. The main principle of the MTI is that the phase of a moving aerial vehicle changes with time, whereas, the phase of a static aerial vehicle remains constant.…”

Section: E Motion Filteringmentioning

confidence: 99%

A Survey on Radar Techniques for Detection,Tracking, and Classification of Aerial Threats

Khawaja¹

2022

Preprint

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<p> The use of unmanned aerial vehicles (UAVs) for different applications has increased many folds in recent years. The UAVs are expected to change the future air operations. However, there are instances where the UAVs can be used for malicious purposes. The detection, tracking, and classification of UAVs is challenging compared to manned aerial vehicles (MAVs) mainly due to small size, complex shapes, and ability to fly close to the terrain and in autonomous flight patterns in swarms. In this survey, we will discuss current and future aerial threats, and provide an overview of radar systems to counter such threats. We also study the performance parameters of radar systems for the detection, tracking, and classification of UAVs compared to MAVs. Finally, limitations of radar systems and comparison with other techniques that do not rely on radars for detection, tracking, and classification of aerial threats are also provided. </p>

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“…At present, there are only three parameters available for rotor UAV identification: rotor blade length [11][12][13], number of blades [14][15][16], and number of rotors [17]. The different models of rotor blades reflect different information and can extract different parameters.…”

Section: Introductionmentioning

confidence: 99%

A New Estimation Method for Rotor Size of UAV Based on Peak Time-Shift Effect in Micro-Doppler Lidar

Zhang

Han

et al. 2022

Front. Phys.

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The aim of this study was to solve the problem that the existing identification parameters of rotor unmanned aerial vehicles (UAVs) are few and limited by the detection mode, and an identification method for estimating the rotor blade width based on the peak time-shift effect is proposed for the first time. Taking the width of the rotor blade as the parameter to identify the rotor of UAVs, the time-shift effect and its relationship with rotor blade width are verified by theoretical analysis and simulation. The proposed time-shift method has the characteristics of high-precision extraction of rotor width, and its effectiveness is verified by simulation and experiments. The aspect ratio of the rotor is accurately extracted based on the proposed time-shift method under the condition of an unknown pitch angle. Simulation results show that the estimation accuracy of the width and aspect ratio is up to 98 and 98.4%, respectively. The experimental results show that the relative errors of the width and aspect ratio are less than 7 and 4%, respectively. This study provides the theoretical basis and technical support for the high-accuracy identification of rotorcraft UAVs.

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Modeling Small UAV Micro-Doppler Signature Using Millimeter-Wave FMCW Radar

Cited by 23 publications

References 26 publications

Model-Based Data Augmentation Applied to Deep Learning Networks for Classification of Micro-Doppler Signatures Using FMCW Radar

Model-Based Data Augmentation Applied to Deep Learning Networks for Classification of Micro-Doppler Signatures Using FMCW Radar

A Survey on Radar Techniques for Detection,Tracking, and Classification of Aerial Threats

A New Estimation Method for Rotor Size of UAV Based on Peak Time-Shift Effect in Micro-Doppler Lidar

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