Off-grid sparse ISAR imaging by Hlog-DCD algorithm

Tianyun Wang,Xinfei Lu,Zhendong Xi,Zhiqiang Song,Haiming Tong,Weidong Chen

Radar Conference（2014）

引用 2|浏览3

暂无评分

摘要

In this paper, a novel computationally efficient algorithm is proposed to achieve high resolution for inverse synthetic aperture radar (ISAR) imaging in the compressive sensing (CS) framework, which is based on dichotomous coordinate descent (DCD) iterations, homotopy, and non-convex regularization. Since traditional CS based methods have to assume that unknown scatterers exactly lie on the pre-discretized grids, otherwise their inversion performance would degrade considerably. Herein we present Hlog-DCD algorithm combined with grid refinement technique to provide performance improvement of the image reconstruction for off-grid target. Experimental results verify the effectiveness of the proposed approach and related analysis.

查看译文

关键词

compressed sensing,image reconstruction,radar imaging,synthetic aperture radar,cs based methods,hlog-dcd algorithm,compressive sensing framework,dichotomous coordinate descent iterations,homotopy,inverse synthetic aperture radar imaging,inversion performance,nonconvex regularization,off-grid sparse isar imaging,off-grid target,prediscretized grids,isar imaging,grid refinement technique,sparse reconstruction,imaging,sparse matrices,image resolution,vectors

AI 理解论文

溯源树

样例

生成溯源树，研究论文发展脉络

Chat Paper

正在生成论文摘要