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. This network takes as input raw range–Doppler radar data and predicts their class (car, person, or drone). The processing is based on constant false alarm rate detection stage, followed by a convolutional neural network that performs the recognition. The proposed system only uses a persistent range–Doppler radar, avoiding the restrictions of the optical sensors, usually required for the recognition part. 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. However, this fact also opens the door for malicious use. 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. Experiment outcome shows LSTM-ALRO achieves much better drone detection accuracies when compared with the existing CNN-based drone classification model. Further, present adaptive learning rate optimizing (ALRO) model for training the LSTM model. This paper present an im- proved long short-term memory (LSTM) by introducing a weight optimization model that can reduce computation overhead by not allowing the gradient to not flow through hidden states of the LSTM model. Thus, when using CNN-based drone classification under a highly dynamic environment exhibit poor classification accuracy. Converting every signature into an image induces additional computation overhead further CNN model is trained considering fixed learning rate. Existing drone classification converts the RCS signature into images and then performs drone classification using a convolution neural network (CNN). Using radar cross-section (RCS) signature enables us to detect malicious drones and suitable action can be taken by respective authorities. The adoption mmWave technology in radar systems enables better resolution and aid in detecting smaller drones. This paper presents drone classification at millimeter-wave (mmWave) radars using the deep learning (DL) technique.
0 Comments
Leave a Reply. |