Open AccessPolarimetric HRRP recognition based on feature‐guided Transformer model
Author(s) -
Zhang Liang,
Han Chang,
Wang Yanhua,
Li Yang,
Long Teng
Publication year - 2021
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12225
Subject(s) - artificial intelligence , pattern recognition (psychology) , computer science , transformer , feature (linguistics) , radar , feature extraction , classifier (uml) , deep learning , radar imaging , polarimetry , similarity (geometry) , similarity measure , machine learning , engineering , scattering , image (mathematics) , telecommunications , linguistics , philosophy , voltage , electrical engineering , physics , optics
Polarimetric high‐resolution range profile (HRRP) holds great potential for radar automatic target recognition (RATR) owing to its capability of providing both polarimetric and spatial scattering information. In recent years, deep learning (DL) has obtained state‐of‐the‐art results in many classification tasks and has drawn great attention in the RATR field. However, as one of the most challenging tasks in RATR, small training sample case will restrict the application of DL because its superior performance generally depends on a large number of training samples. A feature‐guided deep model based on Transformer framework is proposed for polarimetric HRRP recognition with limited training samples. In the proposed model, artificial features are introduced to the attention module to guide the model focus on the range cells of HRRP with more target scattering information so as to reduce the dependence of the model on the number of training samples. Several different approaches are also studied to measure the similarity between artificial features and HRRP data to further improve the learning capacity of the model. Experimental results demonstrate that the proposed feature‐guided Transformer model modifying by Cosine similarity measure is able to achieve a better performance for polarimetric HRRP recognition with limited training samples.