Yang Jungang, Jin Tian, Xiao Chao, Huang Xiaotao
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China.
Sensors (Basel). 2019 Jul 13;19(14):3100. doi: 10.3390/s19143100.
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.
近年来，基于稀疏性驱动的正则化和压缩感知（CS）的雷达成像方法引起了广泛关注。本文介绍了该领域的基本概念。此外，我们将在统一的数学框架中描述稀疏性驱动的正则化和基于CS的雷达成像方法，以及其他方法。这将从清晰的数学角度为读者提供雷达成像理论和方法的系统概述。本文介绍的方法包括最小方差无偏估计、最小二乘（LS）估计、贝叶斯最大后验（MAP）估计、匹配滤波、正则化和CS重建。还分析了这些方法的特点及其联系。基于稀疏性驱动的正则化和CS的雷达成像方法是一个活跃的研究领域；仍然存在许多未解决或未公开的问题，如采样方案、计算复杂度、稀疏表示、杂波影响和模型误差补偿。我们将总结与这些问题相关的挑战以及最新进展。
