Intelligent radar HRRP target recognition based on CNN-BERT model
Penghui Wang, Ting Chen, Jun Ding, Mian Pan, Sanding Tang
Abstract
Abstract Stable and reliable feature extraction is crucial for radar high-resolution range profile (HRRP) target recognition. Owing to the complex structure of HRRP data, existing feature extraction methods fail to achieve satisfactory performance. This study proposes a new deep learning model named convolutional neural network–bidirectional encoder representations from transformers (CNN-BERT), using the spatio–temporal structure embedded in HRRP for target recognition. The convolutional token embedding module characterizes the local spatial structure of the target and generates the sequence features by token embedding. The BERT module captures the long-term temporal dependence among range cells within HRRP through the multi-head self-attention mechanism. Furthermore, a novel cost function that simultaneously considers the recognition and rejection ability is designed. Extensive experiments on measured HRRP data reveal the superior performance of the proposed model.