DopplerNet: a convolutional neural network for recognising targets in real scenarios using a persistent range-Doppler radar

被引:38
作者
Roldan, Ignacio [1 ]
del-Blanco, Carlos R. [2 ]
de Quevedo, Alvaro Duque [3 ]
Urzaiz, Fernando Ibanez [3 ]
Menoyo, Javier Gismero [3 ]
Lopez, Alberto Asensio [3 ]
Berjon, Daniel [2 ]
Jaureguizar, Fernando [2 ]
Garcia, Narciso [2 ]
机构
[1] ART, Madrid, Spain
[2] Univ Politecn Madrid, ETSI Telecomunicac, Informat Proc & Telecommun Ctr, Grp Tratamiento Imagenes, Madrid, Spain
[3] Univ Politecn Madrid, ETSI Telecomunicac, Informat Proc & Telecommun Ctr, Grp Microondas & Radar, Madrid, Spain
关键词
radar detection; Doppler radar; object detection; feature extraction; radar imaging; geophysical image processing; radar target recognition; neural nets; convolutional neural network; persistent range-Doppler radar; drone; safe cost-effective solution; police; security agencies; recognition part; CFAR detection stage; input raw range-Doppler radar data; extensive controlled trial test campaign; range-Doppler radar database; CLASSIFICATION; UAVS;
D O I
10.1049/iet-rsn.2019.0307
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In the past few years, the commercial use of drones has exploded, since they are a safe and cost-effective solution for many kinds of problems. However, this fact also opens the door for malicious use. This work presents a novel system able to detect and recognise drones from other targets, allowing the police and security agencies to deal with this new aerial thread. The proposed system only uses a persistent range-Doppler radar, avoiding the restrictions of the optical sensors, usually required for the recognition part. The processing is based on constant false alarm rate detection stage, followed by a convolutional neural network that performs the recognition. This network takes as input raw range-Doppler radar data and predicts their class (car, person, or drone). For this purpose, an extensive controlled trial test campaign has been performed, resulting in a novel dataset with more than 17,000 samples of drones, cars, and people, acquired in real outdoor scenarios. As far as authors' knowledge, this is the first range-Doppler radar database for the recognition of drones and other targets. The high-accuracy results (99.48%) suggest that this system could be successfully used in security and defence applications to discriminate between drones and other entities.
引用
收藏
页码:593 / 600
页数:8
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