TY - GEN
T1 - Targets recognition based on deep learning
AU - Liu, Huan
AU - Kuang, Lei
AU - Liu, Qing Huo
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - A deep learning based recognition algorithm to identify various targets was proposed in this paper. Compared with traditional machine learning, deep learning can extract the features of recognized targets better and obtain higher accuracy. We first simulate electromagnetic scattering of targets and acquired the scattering electric field of targets at different frequencies and scattering angles. Then we use the scattering electric field to get the ISAR image. Then we input ISAR images to the deep convolutional neural networks for training, and extract the deeper features of the targets. In order to improve the accuracy of recognition, we combine different polarization ISAR images as one sample. Numerical results show that the average recognition accuracy of our proposed method is 99.72%, which verifies the effectiveness of the method.
AB - A deep learning based recognition algorithm to identify various targets was proposed in this paper. Compared with traditional machine learning, deep learning can extract the features of recognized targets better and obtain higher accuracy. We first simulate electromagnetic scattering of targets and acquired the scattering electric field of targets at different frequencies and scattering angles. Then we use the scattering electric field to get the ISAR image. Then we input ISAR images to the deep convolutional neural networks for training, and extract the deeper features of the targets. In order to improve the accuracy of recognition, we combine different polarization ISAR images as one sample. Numerical results show that the average recognition accuracy of our proposed method is 99.72%, which verifies the effectiveness of the method.
UR - https://www.scopus.com/pages/publications/85082502475
U2 - 10.1109/PIERS-Fall48861.2019.9021700
DO - 10.1109/PIERS-Fall48861.2019.9021700
M3 - 会议稿件
AN - SCOPUS:85082502475
T3 - 2019 Photonics and Electromagnetics Research Symposium - Fall, PIERS - Fall 2019 - Proceedings
SP - 1393
EP - 1400
BT - 2019 Photonics and Electromagnetics Research Symposium - Fall, PIERS - Fall 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 Photonics and Electromagnetics Research Symposium - Fall, PIERS - Fall 2019
Y2 - 17 December 2019 through 20 December 2019
ER -