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.

稳定可靠的特征提取对于雷达高分辨率距离剖面（HRRP）目标识别至关重要。由于 HRRP 数据结构复杂，现有的特征提取方法无法达到令人满意的性能。本研究提出了一种新的深度学习模型，称为卷积神经网络——来自转换器的双向编码器表示（CNN-BERT），使用嵌入在 HRRP 中的时空结构进行目标识别。卷积令牌嵌入模块表征目标的局部空间结构，并通过令牌嵌入生成序列特征。BERT 模块通过多头自注意机制捕获 HRRP 内范围单元之间的长期时间依赖性。此外，设计了一种同时考虑识别和拒绝能力的新成本函数。对测量的 HRRP 数据进行的大量实验揭示了所提出模型的优越性能。