Hybrid AI-enabled Method for UAS and Bird Detection and Classification

Zhang, Xu; Mehta, Varun; Bolić, Miodrag; Mantegh, Iraj

doi:10.1109/smc42975.2020.9282965

Cited by 17 publications

(10 citation statements)

References 17 publications

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“…In particular, the classification of UAVs compared to birds is challenging due to similar features [ 83 ]. Many techniques are available in the literature for the differentiation of small UAVs from birds [ 84 , 85 ].…”

Section: Radar Systems For Uav Detection Tracking and Classificationmentioning

confidence: 99%

“…Other modern radars include interferometry radars [ 69 , 119 ] for detection and tracking of UAVs. A combination of different radar techniques can also be used for the detection of modern aerial threats [ 51 , 52 , 85 ]. Micro-Doppler radars are popular for UAV classification [ 88 ].…”

Section: Future Research Directions For Uav Utilization and Their Det...mentioning

confidence: 99%

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Threats from and Countermeasures for Unmanned Aerial and Underwater Vehicles

Khawaja

Semkin

Ratyal

et al. 2022

Sensors

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The use of unmanned aerial vehicles (UAVs) for different applications has increased tremendously during the past decade. The small size, high maneuverability, ability to fly at predetermined coordinates, simple construction, and affordable price have made UAVs a popular choice for diverse aerial applications. However, the small size and the ability to fly close to the terrain make the detection and tracking of UAVs challenging. Similarly, unmanned underwater vehicles (UUVs) have revolutionized underwater operations. UUVs can accomplish numerous tasks that were not possible with manned underwater vehicles. In this survey paper, we provide features and capabilities expected from current and future UAVs and UUVs, and review potential challenges and threats due to use of such UAVs/UUVs. We also overview the countermeasures against such threats, including approaches for the detection, tracking, and classification of UAVs and UUVs.

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Section: Radar Systems For Uav Detection Tracking and Classificationmentioning

confidence: 99%

Section: Future Research Directions For Uav Utilization and Their Det...mentioning

confidence: 99%

Threats from and Countermeasures for Unmanned Aerial and Underwater Vehicles

Khawaja

Semkin

Ratyal

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…In addition to DCNNs, Recurrent Neural Networks (RNNs) have also been exploited for target classification based on micro-Doppler effect [55][56][57][58][59]. Out of different RNN types, Long Short-Term Memory (LSTM) networks are popular, as they can overcome the issue of vanishing / exploding gradient problem [60] and are able to learn both long and short data sequences compared to other RNN architectures [61].…”

Section: Deep Learning Classification Algorithmsmentioning

confidence: 99%

Micro-Doppler Based Target Recognition With Radars: A Review

et al. 2022

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With the deployment of radar in versatile scenarios and a wide variety of potential targets, demand for automatic classification of various targets is increasing. The wide variety of radar signatures from physically smaller targets due to lower velocity / radar cross-section thresholds and the increased deployment of radar-based sensors do crowd the radar screen with misinterpreted targets. Micro-Doppler signatures have been widely employed by researchers for the recognition of those targets that exhibit micro-motions. This review article presents the evolution and recent advances in radar micro-Doppler based signature analysis and feature extraction. A review of the micro-Doppler-based target classification techniques along with their applications in defense and commercial sectors, has

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“…In work [15] the authors analysed use of recurrent neural networks (RNN) for drone identification based on trajectory information. 5000 simulated flight tracks of birds and drones were generated for this purpose using different models.…”

Section: Related Workmentioning

confidence: 99%

Radar Discrimination of Small Airborne Targets Through Kinematic Features and Machine Learning

Doumard

Riesco

Petrunin

et al. 2022

2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC)

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This work studies binary classification problem for small airborne targets (drones vs other) by means of their trajectory analysis. For this purpose a set of the kinematic features extracted from drone trajectories using radar detections with a classification scheme that utilises Random Forests is proposed. The development is based on experimental data acquired from the Holographic radar from Aveillant Ltd. An approach for real-time classification is proposed, where an adaptive sliding window procedure is employed to make predictions over time from trajectories. Several models utilising different kinematic features (angle, slope, velocity, and their combination) are studied. The best model achieves an accuracy of more than 95%. In addition, fundamental issues with imbalanced datasets in the context of this topic are raised and illustrated using the collected data.

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Hybrid AI-enabled Method for UAS and Bird Detection and Classification

Cited by 17 publications

References 17 publications

Threats from and Countermeasures for Unmanned Aerial and Underwater Vehicles

Threats from and Countermeasures for Unmanned Aerial and Underwater Vehicles

Micro-Doppler Based Target Recognition With Radars: A Review

Radar Discrimination of Small Airborne Targets Through Kinematic Features and Machine Learning

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