DopplerNet: a convolutional neural network for recognising targets in real scenarios using a persistent range–Doppler radar
Ignacio Roldan, Carlos R. del‐Blanco, Álvaro Duque de Quevedo, Fernando Ibañez Urzaiz, Javier Gismero Menoyo, A. Asensio‐López, Daniel Berjón, Fernando Jaureguizar, Narciso Garcı́a
Abstract
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.