Target-attentional CNN for Radar Automatic Target Recognition with HRRP

• Published in: Signal processing 2022-07, Vol.196, p.108497, Article 108497
• Main Authors: Chen, Jian, Du, Lan, Guo, Guanbo, Yin, Linwei, Wei, Di
• Format: Article
• Language: English
• Subjects: attention mechanism; Gated recurrent unit (GRU); High-resolution range profile (HRRP); One-dimensional convolutional neural network (1-D CNN); Radar target recognition
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2022.108497

•A deep model called TACNN is proposed for the feature extraction of radar HRRP.•The TACNN is a combination of 1-D CNN and Bi-GRU based attention mechanism.•The 1-D CNN is utilized to excavate abundant local structural features of HRRP.•The attention mechanism can automatically pick out the valuable local features.•Experimental results on measured HRRP data show the effectiveness of our method. In this paper, a target-attentional convolutional neural network (TACNN) combining the convolutional neural network (CNN) and attention mechanism is proposed for radar high-resolution range profile (HRRP) target recognition. The TACNN takes one-dimensional CNN (1-D CNN) as the feature extractor and has the capability to excavate abundant local structural features of data. However, the HRRP contains non-target areas, where the information is useless or even unfavorable. Furthermore, different parts of HRRP target regions should have differences in contribution to the recognition task. Therefore, it is an inadvisable approach that treats all local features alike and directly uses them for the subsequent target recognition, which is adopted by a lot of models, such as the conventional CNN. To tackle this problem, the TACNN introduces the attention mechanism on the basis of 1-D CNN. In detail, the constructed attention module adaptively assigns a weight to each local feature of HRRP so as to locate the target areas and meanwhile enhance the interest of model in valuable target information. Specially, the attention mechanism in TACNN is realized via a bidirectional gated recurrent unit (Bi-GRU) network, where the attention coefficients used for weighting up local features are generated with full consideration of sequential relationship among different regional features in HRRP. Therefore, the learned attention coefficients in our TACNN can better represent the importance of each local feature to the recognition task, ultimately beneficial for the discovery of target information with more discriminability. Experimental results on measured HRRP data show that the proposed model can get more effectiveness in target recognition than related methods.