Sparsity and Compressed Sensing in Radar Imaging

作者：Potter Lee C^*; Ertin Emre; Parker Jason T; Cetin Muejdat

来源：Proceedings of the IEEE, 2010, 98(6): 1006-1020.

DOI：10.1109/JPROC.2009.2037526

• Moving target indication
• penalized least squares
• radar ambiguity function
• random arrays
• sparse reconstruction
• synthetic aperture radar

Remote sensing with radar is typically an ill-posed linear inverse problem: a scene is to be inferred from limited measurements of scattered electric fields. Parsimonious models provide a compressed representation of the unknown scene and offer a means for regularizing the inversion task. The emerging field of compressed sensing combines nonlinear reconstruction algorithms and pseudorandom linear measurements to provide reconstruction guarantees for sparse solutions to linear inverse problems. This paper surveys the use of sparse reconstruction algorithms and randomized measurement strategies in radar processing. Although the two themes have a long history in radar literature, the accessible framework provided by compressed sensing illuminates the impact of joining these themes. Potential future directions are conjectured both for extension of theory motivated by practice and for modification of practice based on theoretical insights.

• 出版日期2010-6