High‐resolution range profile (HRRP) has obtained intensive attention in radar target recognition and convolutional neural networks (CNNs) are among predominant approaches to deal with HRRP recognition problems. However, most CNNs are designed by the rule‐of‐thumb and suffer from much more computational complexity. Aiming at enhancing the channels of one‐dimensional CNN (1D‐CNN) for extracting efficient structural information oftargets form HRRP and reducing the computation complexity, we propose a novel framework for HRRP‐based target recognition based on 1D‐CNN with channel attention and channel pruning. By introducing an aggregation‐perception‐recalibration (APR) block for channel attention to the 1D‐CNN backbone, channels in each 1D convolutional layer can adaptively learn to recalibrate the extracted features for enhancing the structural information captured from HRRP. To avoid rule‐of‐thumb design and reduce the computation complexity of 1D‐CNN, we proposed a new method incorporated withthe global best leading artificial bee colony (GBL‐ABC) to prune the original network based on the lottery ticket hypothesis in an automatic and heuristic manner. The extensive experimental results on the measured data illustrate that the proposed algorithm achievesthe superiorrecognition rate by combing APR and GBL‐ABC simultaneously.

中文翻译：

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一维卷积神经网络通过自适应特征重新校准和自动通道剪枝进行高分辨率范围轮廓识别

高分辨率距离剖面（HRRP）在雷达目标识别中得到了广泛关注，卷积神经网络（CNN）是处理 HRRP 识别问题的主要方法之一。然而，大多数 CNN 是根据经验法则设计的，并且计算复杂性要高得多。为了增强一维CNN（1D-CNN）的通道以从HRRP中提取目标的有效结构信息并降低计算复杂度，我们提出了一种基于1D-CNN的基于HRRP的目标识别新框架，具有通道注意力和通道修剪。通过引入聚合-感知-重新校准 (APR) 块，用于对 1D-CNN 主干的通道注意力，每个一维卷积层中的通道可以自适应地学习重新校准提取的特征，以增强从 HRRP 捕获的结构信息。为了避免经验法则设计并降低 1D-CNN 的计算复杂度，我们提出了一种结合全球最佳领先人工蜂群 (GBL-ABC) 的新方法，基于彩票假设自动剪枝原始网络。和启发式的方式。对实测数据的大量实验结果表明，该算法通过同时结合 APR 和 GBL-ABC 实现了优异的识别率。我们提出了一种结合全球最佳领先人工蜂群（GBL-ABC）的新方法，以自动和启发式的方式基于彩票假设修剪原始网络。对实测数据的大量实验结果表明，该算法通过同时结合 APR 和 GBL-ABC 实现了优异的识别率。我们提出了一种结合全球最佳领先人工蜂群（GBL-ABC）的新方法，以自动和启发式的方式基于彩票假设修剪原始网络。对实测数据的大量实验结果表明，该算法通过同时结合 APR 和 GBL-ABC 实现了优异的识别率。