High‐Resolution Range Profile Sequence Recognition Based on Transformer with Temporal–Spatial Fusion and Label Smoothing

Abstract

Radar high‐resolution range profile (HRRP) is widely used in radar automatic target recognition due to its advantages such as easy availability, convenient processing, and small storage space. Current recognition methods for HRRP sequences mainly focus on the temporal information of HRRP sequences, which cannot fully utilize the temporal and spatial information contained in HRRP sequences. Moreover, most of these methods fail in long‐range modeling and global information extraction of HRRP sequences. To solve above problems, a HRRP sequence recognition method based on transformer with temporal–spatial fusion and label smoothing (TSF–transformer–LS) is proposed. TSF–transformer–LS contains temporal transformer blocks and spatial transformer blocks, which are used to extract deep global features of HRRP sequences in the time domain and space domain, respectively. Then, an attention fusion mechanism is developed to realize the adaptive fusion of temporal and spatial features. Moreover, label smoothing is used to add noise to sample labels, which can solve the overfitting problem of transformer caused by a large amount of noise hidden in HRRP in real scenes. Experiments on MSTAR, a standard dataset, show that the proposed method outperforms other methods in recognition performance. Furthermore, the effectiveness and interpretability of the method are explored.