Due to the thinner resolution range of broadband radar, ship recognition issues arise such that minor fluctuations within the targeted area significantly affect the high-resolution range profile (HRRP) of ships. Especially in the presence of reflector decoys around the surroundings of a ship, the HRRP of mixed targets might take a vastly different shape than of single ship, which makes it difficult to capture the effective features for ship identification. This article proposes a novel radar target recognition model based on parallel neural networks. The framework of this model consists of two stages: the data preprocessing and the parallel neural network. The data preprocessing stage effectively solves the sensitivity issue of HRRP and maps one-dimensional HRRP into a two-dimensional image. The second stage employs CNN and bidirectional LSTM to extract overall envelope features and temporal features, respectively. The parallel features are then processed by the Squeeze Excitation (SE) block to enhance critical information. The experimental results, based on HRRP data from mixed targets of ships and reflector decoys, demonstrate that the proposed model outperforms other methods in recognition performance and is quite robust against small sample sets, high noise, and large amounts of decoy jamming.

中文翻译：

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基于CNN-BiLSTM-SE模型的抗诱饵干扰船舶HRRP目标识别

由于宽带雷达的分辨率范围较窄，会出现船舶识别问题，目标区域内的微小波动会严重影响船舶的高分辨率距离剖面（HRRP）。特别是在船舶周围存在反射体诱饵的情况下，混合目标的 HRRP 形状可能与单个船舶的 HRRP 形状有很大不同，这使得捕获船舶识别的有效特征变得困难。本文提出了一种基于并行神经网络的新型雷达目标识别模型。该模型的框架由两个阶段组成：数据预处理和并行神经网络。数据预处理阶段有效解决了HRRP的敏感性问题，将一维HRRP映射为二维图像。第二阶段采用CNN和双向LSTM分别提取总体包络特征和时间特征。然后，并行特征由挤压激励 (SE) 块进行处理，以增强关键信息。基于船舶和反射器诱饵混合目标的HRRP数据的实验结果表明，该模型在识别性能上优于其他方法，并且对于小样本集、高噪声和大量诱饵干扰具有相当的鲁棒性。