Machine and Deep Learning for Drone Radar Recognition by Micro-Doppler and Kinematic criteria

Barbaresco, Frédéric; Brooks, Daniel; Adnet, Claude

doi:10.1109/radarconf2043947.2020.9266371

Cited by 12 publications

(12 citation statements)

References 14 publications

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“…The classification is also aided by AI algorithms. Deep learning AI algorithms help to accurately identify the type of the aerial vehicle [58], [59].…”

Section: Popular Radar Signal Processing Techniquesmentioning

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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“…The classification is also aided by AI algorithms. Deep learning AI algorithms help to accurately identify the type of the aerial vehicle [58], [59].…”

Section: Popular Radar Signal Processing Techniquesmentioning

confidence: 99%

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

Khawaja¹

2022

Preprint

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“…The authors of ref. [130] use the raw time series, spectrogram, deep latent vectors and covariance data to design an AI-based global model using forward connected NNs. Even though the dataset distribution skewed the distribution between object classes, the designed model performs multi-class classification with high precision and recall values.…”

Section: Machine Learning For Non-rrm Tasks-selected Literature Surveymentioning

confidence: 99%

Artificial intelligence meets radar resource management: A comprehensive background and literature review

Hashmi

Akbar

Adve

et al. 2022

IET Radar Sonar & Navi

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A multi‐function radar is designed to perform disparate functions, such as surveillance, tracking, fire control, amongst others, within a limited resource (time, frequency, and energy) budget. A radar resource management (RRM) module within a radar system makes decisions on prioritisation, parameter selection, and scheduling of associated tasks. However, optimal RRM algorithms are generally computationally complex and operational radars resort to heuristics. On the other hand, algorithms based on artificial intelligence (AI) have been shown to yield near‐optimal radar resource allocation results at manageable computational complexity. This survey study aims at enabling researchers and practitioners better understand the application of AI in RRM‐related problems by providing a thorough literature review of AI‐based RRM techniques. We first provide background concepts in RRM followed by a brief review of Symbolic‐AI techniques for RRM. We mainly focus on the applications of state‐of‐the‐art machine learning techniques to RRM. We emphasise on the recent findings and their potential within practical RRM scenarios for real‐time resource allocation optimisation. The study concludes with a discussion of open research problems and future research directions in the light of the presented survey.

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“…The algorithm uses the spectral kurtosis technique and the principal component analysis. Machine and deep learning are used for the detection of UAVs using micro-Doppler signatures, and kinematic criteria in [192]. Enhanced UAV detection and feature extraction are carried out using Doppler and micro-Doppler signatures in [193].…”

Section: Classification Using Micro-doppler Radar Signaturesmentioning

confidence: 99%

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

Khawaja¹

2022

Preprint

View full text Add to dashboard Cite

show abstract

Machine and Deep Learning for Drone Radar Recognition by Micro-Doppler and Kinematic criteria

Cited by 12 publications

References 14 publications

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

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

Artificial intelligence meets radar resource management: A comprehensive background and literature review

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

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