Classification of Bird and Drone Targets Based on Motion Characteristics and Random Forest Model Using Surveillance Radar Data

Liu, Jia; Xu, Qinguang; Chen, Wei Shi

doi:10.1109/access.2021.3130231

Cited by 26 publications

(12 citation statements)

References 26 publications

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“…• Classification of Bird and Drone Targets Based on Motion Characteristics and Random Forest Model Using Surveillance Radar Data [33] -Authors extract 5 features from the data collected using bird surveillance radars located in airports. The five features are speed, speed std, direction, maneuverability, and oscillation.…”

Section: • Classification Of Birds and Uavs Based On Radar Polarimetr...mentioning

confidence: 99%

Bird/Drone Detection and Classification using Classical and Deep Learning Methods

Zitar

Kassab

Fallah

et al. 2023

Preprint

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Machine learning techniques have always been a strong candidate for solving complex recognition problems. Drone/Bird detection and classification is one of the most challenging tasks in recent years. Both drones and birds come in different sizes, velocities, and behaviors. The lack of bird images and videos is tackled in this work. Deep learning, classical machine learning techniques such as Support Vector Machines (SVM) and Random Forest (RF) in addition to shallow neural network (NN) learning methods are used. Combined open-source data sets and labeled bird images data sets are used in training and testing for detection and classification. In particular, several deep learning methods are used in the detection of RGB and IR drone images. They were compared with the new SSD-AdderNet which showed the best results in the detection of IR images while exhibiting the least complexity. The SVM proved to be the best in classification.

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Section: • Classification Of Birds and Uavs Based On Radar Polarimetr...mentioning

confidence: 99%

Bird/Drone Detection and Classification using Classical and Deep Learning Methods

Zitar

Kassab

Fallah

et al. 2023

Preprint

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“…Although RCS signatures can be used to implement drone detection and classification, it requires upgrading the radar calibration and other configurations. Authors in [81] proposed an independent method which does not uses RCS signatures. The method uses flight mechanic and behavior modes of drones and birds represented as discrete motion characteristics and derive five features i.e., mean speed, Standard Deviation (SD) of speed, SD of heading, maneuverability factor, and oscillation factor.…”

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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“…Maritime radar systems with additional post-processing can be used for birds detection. In [180], motion characteristics of the birds and UAVs collected by a surveillance radar are used for their classification using a random forest classifier. The radar system used in [180] is a bird surveillance radar system installed at airports and operates at S-band.…”

Section: Classification Of Aerial Vehicles Using Radar Systemsmentioning

confidence: 99%

“…In [180], motion characteristics of the birds and UAVs collected by a surveillance radar are used for their classification using a random forest classifier. The radar system used in [180] is a bird surveillance radar system installed at airports and operates at S-band. The measurements for the classification of UAVs and birds using L-band staring radar are provided in [181] in an urban area.…”

Section: Classification Of Aerial Vehicles Using Radar Systemsmentioning

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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Classification of Bird and Drone Targets Based on Motion Characteristics and Random Forest Model Using Surveillance Radar Data

Cited by 26 publications

References 26 publications

Bird/Drone Detection and Classification using Classical and Deep Learning Methods

Bird/Drone Detection and Classification using Classical and Deep Learning Methods

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

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

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