摘要
混沌压缩感知是一种利用混沌系统实现非线性测量,通过混沌脉冲同步和参数估计技术实现信号重构的压缩感知理论。针对混沌压缩感知重构系统中采用l1范数正则化信号系数导致在信号稀疏水平较高时重构性能急剧下降的问题,利用lp(0 p 1)范数来正则化信号系数,将重构系统中的非线性约束l1范数最小化问题替换为非线性约束lp范数最小化问题,并提出-正则迭代再加权非线性最小二乘算法进行求解。以Henon混沌为例,研究了频域稀疏信号的重构性能,数值模拟表明lp范数正则化能够准确重构出比l1范数正则化时稀疏水平更高的信号。
Chaotic Compressive Sensing （ChaCS） is a nonlinear compressive sensing theory which uses chaos systems to measure signals and performs the signal reconstruction by chaotic impulsive synchronization and parameter estimation. Existing ChaCS reconstruction system uses /l-norm to regularize signal coefficients and its reconstruction performance degrades rapidly as the signal sparsity level increases, lp-norm （0〈p〈1） was used to regularize signal coefficients and a c -regularized iterative reweighted nonlinear least square algorithm was proposed to solve the nonlinearly constrained lp -norm minimization. The Henon system was taken as an example to expose the reconstruction performance of frequency sparse signals. Numerical simulations illustrate that lp -norm regularization can accurately reconstruct the signal with higher sparsity level than l1-norm regularization.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2013年第11期2667-2671,共5页
Journal of System Simulation
基金
国家自然科学基金(60971090,61171166,61101193)
关键词
混沌压缩感知
重构
易范数正则化
参数估计
chaotic compressive sensing
reconstruction
lp -norm regularization
parameter estimation
