基于深度学习的雷达成像研究进展

Research progress of radar imaging based on Deep Learning

摘要:

深度学习(DL)在语音识别、图像物体识别上取得了卓越的成效，深度学习代替传统处理技术，成为了研究该领域的主要处理方法。在雷达领域，深度学习用于雷达目标识别和分类，也取得了很好的效果，进而，人们试图将深度学习用于雷达成像。本文根据近几年所公开的文献资料，按照雷达成像的特点，分类介绍深度学习用于雷达成像的研究进展；之后，对深度学习用于雷达成像的可行性、样本选取、泛化以及成像质量的评价等开放性问题提出了作者的设想，并对深度学习用于雷达成像进行了展望。

Abstract:

Deep Learning(DL) has achieved remarkable results in speech recognition and image object recognition, which has become the main processing method in these fields instead of traditional processing technology. DL is applied to radar target recognition and classification, and has achieved good results as well. Therefore, DL is tried to be applied in radar imaging. Based on the published literatures in recent years and the characteristics of radar imaging, the research progresses of DL in radar imaging are introduced. Its feasibility, sample selection, the generalization, and the evaluation of imaging quality are analyzed. The application prospects of DL in radar imaging are outlooked.