Dictionary Learning for Radar Classification of Multiple Micro-Drones

Zhang, Wenyu; Li, Gang; Baker, Chris

doi:10.1109/radar41533.2019.171299

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…Naturally, depending on the ultimate purpose and intentions, one must adopt an appropriate UAV [22]. Comparably by size, there are nano [16] (Figure 3a), micro [17] (Figure 3b), mini [18] (Figure 3c), small [19] (Figure 3d), medium [20] (Figure 3e), and large [21] (Figure 3f) UAVs. Commonly, due to the miniature size, less battery capacitance, and limited weightload capacity, the nano and micro UAVs cannot monitor a vast area.…”

Section: Classifications Of Unmanned Aerial Vehiclementioning

confidence: 99%

A Comparative Study on Application of Unmanned Aerial Vehicle Systems in Agriculture

et al. 2021

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Presently in agriculture, there is much ample scope for drone and UAS (Unmanned Aircraft System) development. Because of their low cost and small size, these devices have the ability to help many developing countries with economic prosperity. The entire aggregation of financial investments in the agricultural area has increased appreciably in recent years. Sooth to say, agriculture remains a massive part of the world’s commercial growth, and due to some complications, the agriculture fields withstand massive losses. Pets and destructive insects seem to be the primary reasons for certain degenerative diseases. It minimizes the potential productivity of the crops. For increasing the quality of the plants, fertilizers and pesticides are appropriately applied. Using UAVs (Unmanned Aerial Vehicles) for spraying pesticides and fertilizing materials is an exuberant contraption. It adequately reduces the rate of health dilemma and the number of workers, which is quite an impressive landmark. Willing producers are also adopting UAVs in agriculture to soil and field analysis, seed sowing, lessen the time and costs correlated with crop scouting, and field mapping. It is rapid, and it can sensibly diminish a farmer’s workload, which is significantly a part of the agricultural revolution. This article aims to proportionally represent the concept of agricultural purposed UAV clear to the neophytes. First, this paper outlines the harmonic framework of the agricultural UAV, and then it abundantly illustrates the methods and materials. Finally, the article portrays the outcome.

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Section: Classifications Of Unmanned Aerial Vehiclementioning

confidence: 99%

A Comparative Study on Application of Unmanned Aerial Vehicle Systems in Agriculture

et al. 2021

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CT-RURnet: a novel network design for radar unmanned aerial vehicles recognition

Jiang,

Li,

Huang

et al. 2024

Meas. Sci. Technol.

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The rapid growth of commercial unmanned aerial vehicles (UAVs) has led to more incidents. These include illegal activities by malicious actors. Such activities pose significant threats to public facilities and people's safety. Therefore, distinguishing UAVs from other targets is a critical measure in preventing potential hazards. This paper proposes an enhanced feature extraction and global modeling model for UAV recognition, named CT-RURnet. The model directly learns from radar echo data, firstly using the Convolutional Neural Networks (CNN) module to quickly extract local features, and then using the Transformer module to establish global connections among these local features. The proposed model primarily focuses on learning the micro-Doppler features produced by targets, which facilitates UAV recognition. The used datasets include both simulated data and the Real Doppler RAD-DAR (RDRD) dataset. The proposed network achieves accuracy rates of 98.7% (10 SNR), 97.9% (5 SNR), and 86.95% (0 SNR) on the simulated dataset under varying SNR conditions and 97.14% on the real dataset. Compared to other baseline models, the CT-RURnet consistently delivers superior results. Ablation experiments are conducted to verify the contribution of each module to the overall network performance.

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Dictionary Learning for Radar Classification of Multiple Micro-Drones

Cited by 2 publications

References 27 publications

A Comparative Study on Application of Unmanned Aerial Vehicle Systems in Agriculture

A Comparative Study on Application of Unmanned Aerial Vehicle Systems in Agriculture

CT-RURnet: a novel network design for radar unmanned aerial vehicles recognition

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