@article{WANG2025, 
author = {Xiaodan WANG and Peng WANG and Yafei SONG and Qian XIANG and Jingtai LI},
title = {HRRP recognition of midcourse ballistic targets based on AF-BiTCN},
year = {2025},
journal = {Journal of Beijing University of Aeronautics and Astronautics},
volume = {51},
number = {2},
pages = {349-359},
keywords = {bidirectional temporal convolutional neural network, recognition of ballistic target, feature fusion, high-resolution range profile, sliding window algorithm},
url = {https://www.sciopen.com/article/10.13700/j.bh.1001-5965.2023.0025},
doi = {10.13700/j.bh.1001-5965.2023.0025},
abstract = {To address the problem of temporal feature extraction and recognition of high-resolution range profiles (HRRP) of midcourse ballistic targets, a recognition method based on bidirectional temporal convolutional networks with additive fusion (AF-BiTCN) was proposed, which could make full use of the bidirectional temporal information of HRRP of midcourse ballistic targets and further improve the recognition performance. Firstly, the HRRP data was processed into a bidirectional sequence by the bidirectional sliding window algorithm. Then, the BiTCN was constructed to extract bidirectional deep temporal features of HRRP in each layer, and the bidirectional features were fused by an additive strategy. Finally, more robust fusion features were utilized to recognize ballistic targets, and the Adam algorithm was used to optimize the convergence speed and stability of AF-BiTCN. The experimental results show that the proposed HRRP recognition method of midcourse ballistic targets based on AF-BiTCN in this paper has higher accuracy and faster recognition speed compared with six methods such as stack long short-term memory (SLSTM), stack gate recurrent unit (SGRU) and so on, and it achieves an accuracy of 96.60% on the test set. Moreover, the proposed method indicates better robustness on noise datasets.}
}