TY - GEN
T1 - A Novel Algorithm for HRRP Target Recognition Based on CNN
AU - Li, Jieqi
AU - Li, Shaojie
AU - Liu, Qi
AU - Mei, Shaohui
N1 - Publisher Copyright:
© 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
PY - 2020
Y1 - 2020
N2 - Compared with traditional methods, deep neural networks can extract deep information of targets from different aspects in range resolution profile (HRRP) radar automatic target recognition (RATR). This paper proposes a new convolutional neural network (CNN) for target recognition based on the full consideration of the characteristics (time-shift sensitivity, target-aspect sensitivity and large redundancy) of radar HRRP data. Using a convolutional layer with the large convolution kernel, large stride, and large grid size max-pooling, the author built a streamlined network, which can get better classification accuracy than other methods. At the same time, in order to make the network more robust, the author uses the center loss function to correct the softmax loss function. The experimental results show that we have obtained a smaller feature within the class and the classification accuracy is also improved.
AB - Compared with traditional methods, deep neural networks can extract deep information of targets from different aspects in range resolution profile (HRRP) radar automatic target recognition (RATR). This paper proposes a new convolutional neural network (CNN) for target recognition based on the full consideration of the characteristics (time-shift sensitivity, target-aspect sensitivity and large redundancy) of radar HRRP data. Using a convolutional layer with the large convolution kernel, large stride, and large grid size max-pooling, the author built a streamlined network, which can get better classification accuracy than other methods. At the same time, in order to make the network more robust, the author uses the center loss function to correct the softmax loss function. The experimental results show that we have obtained a smaller feature within the class and the classification accuracy is also improved.
KW - Convolutional neural network (CNN)
KW - Radar automatic target recognition (RATR)
KW - Range resolution profile (HRRP)
UR - https://www.scopus.com/pages/publications/85083985388
U2 - 10.1007/978-3-030-44751-9_33
DO - 10.1007/978-3-030-44751-9_33
M3 - 会议稿件
AN - SCOPUS:85083985388
SN - 9783030447502
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 397
EP - 404
BT - IoT as a Service - 5th EAI International Conference, IoTaaS 2019, Proceedings
A2 - Li, Bo
A2 - Yang, Mao
A2 - Yan, Zhongjiang
A2 - Zheng, Jie
A2 - Fang, Yong
PB - Springer
T2 - 5th EAI International Conference on IoT as a Service, IoTaaS 2019
Y2 - 16 November 2019 through 17 November 2019
ER -