Sparse Synthetic Aperture Radar Imaging From Compressed Sensing and Machine Learning: Theories, applications, and trends-Reference-Cited by-同舟云学术

Sparse Synthetic Aperture Radar Imaging From Compressed Sensing and Machine Learning: Theories, applications, and trends

Author:

Xu Gang¹^ORCID,Zhang Bangjie¹,Yu Hanwen²^ORCID,Chen Jianlai³^ORCID,Xing Mengdao⁴^ORCID,Hong Wei¹^ORCID

Affiliation:

1. State Key Laboratory of Millimeter Waves, School of Information Science and Engineering, Southeast University, Nanjing, China

2. School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan, China

3. Associate Professor, Central South University, Changsha, China

4. National Laboratory of Radar Signal Processing, Xidian University, Xi’an, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,General Earth and Planetary Sciences,Instrumentation,General Computer Science

Link

Reference210 articles.

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