One‐dimensional convolutional neural networks for high‐resolution range profile recognition via adaptively feature recalibrating and automatically channel pruning
Qian Xiang, Xiaodan Wang, Yafei Song, Lei Lei, Rui Li, Jie Lai
Abstract
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 aggregationperception-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-ofthumb design and reduce the computation complexity