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Compressed Sensing Radar Imaging: Fundamentals, Challenges, and Advances

College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3100;
Received: 3 June 2019 / Revised: 8 July 2019 / Accepted: 11 July 2019 / Published: 13 July 2019
(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)
PDF [8135 KB, uploaded 13 July 2019]


In recent years, sparsity-driven regularization and compressed sensing (CS)-based radar imaging methods have attracted significant attention. This paper provides an introduction to the fundamental concepts of this area. In addition, we will describe both sparsity-driven regularization and CS-based radar imaging methods, along with other approaches in a unified mathematical framework. This will provide readers with a systematic overview of radar imaging theories and methods from a clear mathematical viewpoint. The methods presented in this paper include the minimum variance unbiased estimation, least squares (LS) estimation, Bayesian maximum a posteriori (MAP) estimation, matched filtering, regularization, and CS reconstruction. The characteristics of these methods and their connections are also analyzed. Sparsity-driven regularization and CS based radar imaging methods represent an active research area; there are still many unsolved or open problems, such as the sampling scheme, computational complexity, sparse representation, influence of clutter, and model error compensation. We will summarize the challenges as well as recent advances related to these issues. View Full-Text
Keywords: radar imaging; synthetic aperture radar; compressed sensing; sparse reconstruction; regularization radar imaging; synthetic aperture radar; compressed sensing; sparse reconstruction; regularization

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Yang, J.; Jin, T.; Xiao, C.; Huang, X. Compressed Sensing Radar Imaging: Fundamentals, Challenges, and Advances. Sensors 2019, 19, 3100.

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