@inproceedings{20342845327d45d5beca792e31330ad7,
title = "Lightweight CNN for HRRP Recognition Based on Attention Mechanism Structured Pruning",
abstract = "Deep convolutional neural network (CNN) has been widely investigated for radar target high resolution range profile (HRRP) recognition. However, the deep structures of CNN require high storage and computational capabilities, thus restricting its applications with limited resources. In this paper, we design a lightweight CNN model with structure pruning based on the channel-wise attention mechanism. Specifically, the attention value is used to represent the importance of the filters in CNN. Furthermore, Greedy strategy and fine-tuning are adopted in the pruning process to minimize the loss of model performance. The results of public dataset show that the recognition accuracy of the proposed method decreases by less than 0.13% when the pruning ratio is 80%.",
keywords = "CNN, HRRP recognition, attention mechanism, model fine-tuning, structured pruning",
author = "Yanhua Wang and Zhilong Zhang and Mingchen Yuan and Jiandong Liao and Liang Zhang",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Radar Conference, RADAR 2023 ; Conference date: 06-11-2023 Through 10-11-2023",
year = "2023",
doi = "10.1109/RADAR54928.2023.10371061",
language = "English",
series = "Proceedings of the IEEE Radar Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE International Radar Conference, RADAR 2023",
address = "United States",
}