Comparing Convolutional Neural Network(CNN) models for machine learning-based drone and bird classification of anti-drone system

Oh, Hyun Min; Lee, Hyunki; Kim, Min Young

doi:10.23919/iccas47443.2019.8971699

Cited by 32 publications

(13 citation statements)

References 3 publications

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“…The advantage of this solution is the fact that the drone usually leaves a noticeable thermal trace during the flight due to the high power generated by its automation. In [16], the authors examined the CNN model that is suitable for visible camera-based drone identification. One of the passive methods of UAV detection is the acoustic method, which relies on tracking the object through listening and sensing and then analysing the sound.…”

Section: Uav Detection Technologiesmentioning

confidence: 99%

Multi-Sensory Data Fusion in Terms of UAV Detection in 3D Space

Dudczyk¹,

Czyba

Skrzypczyk

2022

Sensors

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The paper focuses on the problem of detecting unmanned aerial vehicles that violate restricted airspace. The main purpose of the research is to develop an algorithm that enables the detection, identification and recognition in 3D space of a UAV violating restricted airspace. The proposed method consists of multi-sensory data fusion and is based on conditional complementary filtration and multi-stage clustering. On the basis of the review of the available UAV detection technologies, three sensory systems classified into the groups of passive and active methods are selected. The UAV detection algorithm is developed on the basis of data collected during field tests under real conditions, from three sensors: a radio system, an ADS-B transponder and a radar equipped with four antenna arrays. The efficiency of the proposed solution was tested on the basis of rapid prototyping in the MATLAB simulation environment with the use of data from the real sensory system obtained during controlled UAV flights. The obtained results of UAV detections confirmed the effectiveness of the proposed method and theoretical expectations.

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Section: Uav Detection Technologiesmentioning

confidence: 99%

Multi-Sensory Data Fusion in Terms of UAV Detection in 3D Space

Dudczyk¹,

Czyba

Skrzypczyk

2022

Sensors

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“…Drone detection using images or videos captured by camera sensors is based on computer vision algorithms and can be performed by traditional machine learning algorithms [ 9 , 10 ] or deep learning-based algorithms [ 11 , 12 , 13 ]. For this approach, the line-of-sight (LoS) is mandatory and the performance depends on the quality of the image captured that may be degraded by adverse environment conditions such as fog, cloud, dust, or low ambient light.…”

Section: Introductionmentioning

confidence: 99%

New Approach of UAV Movement Detection and Characterization Using Advanced Signal Processing Methods Based on UWB Sensing

Digulescu

Despina-Stoian

Stanescu

et al. 2020

Sensors

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In the last years, the commercial drone/unmanned aerial vehicles market has grown due to their technological performances (provided by the multiple onboard available sensors), low price, and ease of use. Being very attractive for an increasing number of applications, their presence represents a major issue for public or classified areas with a special status, because of the rising number of incidents. Our paper proposes a new approach for the drone movement detection and characterization based on the ultra-wide band (UWB) sensing system and advanced signal processing methods. This approach characterizes the movement of the drone using classical methods such as correlation, envelope detection, time-scale analysis, but also a new method, the recurrence plot analysis. The obtained results are compared in terms of movement map accuracy and required computation time in order to offer a future starting point for the drone intrusion detection.

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“…Due to the fast development of embedded devices and wireless communications, drones have become cheaper and more powerful. For example, drones can not only make outstanding contributions in civil areas such as logistics and agriculture, but also play an important role in responding to search and rescue in emergency G. Qing is with the Nanjing Special Equipment Safety Supervision and Inspection Institute, Nanjing 210019, China (e-mail: shineblue qing@163.com) H. Gacanin is with the Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Aachen 52062, Germany (e-mail: harisg@ieee.org) F. Adachi is with the Research Organization of Electrical Communication (ROEC), Tohoku University, Sendai 980-8577 Japan (e-mail: adachi@ecei.tohoku.ac.jp) disasters [1]- [3]. Drones have become an integral part of our rapid development of society.…”

Section: Introductionmentioning

confidence: 99%

Deep Complex-valued Convolutional Neural Network for Drone Recognition Based on RF Fingerprinting

Gu¹,

Qing²,

Wang³

et al. 2020

Preprint

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Drones-aided ubiquitous applications play more and more important roles in our daily life. Accurate recognition of drones is required in aviation management due to their potential risks and even disasters. Radio frequency (RF) fingerprintingbased recognition technology based on deep learning is considered as one of the effective approaches to extract hidden abstract features from RF data of drones. Existing deep learning-based methods are either a high computational burden or low accuracy. In this paper, we propose a deep complex-valued convolutional neural network (DC-CNN) method based on RF fingerprinting for recognizing different drones. Compared with existing recognition methods, the DC-CNN method has the advantages of high recognition accuracy, fast running time and small network complexity. Nine algorithm models and two datasets are used to represent the superior performance of our system. Experimental results show that our proposed DC-CNN can achieve recognition accuracy of 99.5% and 74.1% respectively on 4 and 8 classes of RF drone datasets.

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Comparing Convolutional Neural Network(CNN) models for machine learning-based drone and bird classification of anti-drone system

Cited by 32 publications

References 3 publications

Multi-Sensory Data Fusion in Terms of UAV Detection in 3D Space

Multi-Sensory Data Fusion in Terms of UAV Detection in 3D Space

New Approach of UAV Movement Detection and Characterization Using Advanced Signal Processing Methods Based on UWB Sensing

Deep Complex-valued Convolutional Neural Network for Drone Recognition Based on RF Fingerprinting

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