无线电工程Issue(5)：20-22,29,4.
压缩感知理论与非凸优化方法研究
Research on Compressed Sensing Theory and Non-convex Optimization Method
摘要
Abstract
Due to the wider bandwidth,some signals are difficult to sample directly.To solve this problem,compressed sensing (CS) provides a feasible way with lower sampling speed.Signals have sparse representation in specific transformation domain.A few projection values obtained through low-speed sampling have already contained the important information for reconstruction. The sparse vector is reconstructed from the projection values based on CS and the original signal is reestablished.A CS reconstruction algo-rithm based on non-convex optimization is also introduced.Comparing to the convex optimization of L1-norm and non-sparse restriction of L2-norm,Lp-norm of non-convex optimization has stronger restriction on sparseness.Experimental results show that CS theory reduces the sampling speed of signal significantly,while the non-convex optimization algorithm has better reconstruction performance.关键词
压缩感知/稀疏性/凸优化/非凸优化/Lp 范数Key words
compressed sensing/sparseness/convex optimization/non-convex optimization/Lp-norm分类
信息技术与安全科学引用本文复制引用
罗纯哲,陈金杰,王蔚东..压缩感知理论与非凸优化方法研究[J].无线电工程,2014,(5):20-22,29,4.基金项目
安徽省自然科学基金资助项目（1208085MF94）；国家自然科学基金资助项目（61272333）；安徽省自然科学基金资助项目（1308085QF99）。 （）
