摘要
混沌压缩感知是一种利用混沌系统实现非线性测量,非线性等式约束l1范数最小化实现信号重构的压缩感知理论;具有实现结构简单,测量数据保密性强等特点。但是,现有算法不能有效地求解非线性等式约束l1范数最小化,求解结果受到额外参数影响。本文通过对非线性约束线性化处理,将非线性等式约束l1范数最小化问题转化为一系列二次锥规划问题,利用线性化迭代二次锥规划算法进行求解,消除了额外参数对信号重构性能的影响,保证了算法的收敛性和提高了信号的重构性能。本文以Henon混沌为例,仿真分析了频域稀疏信号的重构性能,模拟证明了算法的有效性。
Chaotic Compressive Sensing（ChaCS） is a nonlinear compressive sensing theory,which uses chaotic systems to measure signals and performs the signal reconstruction by nonlinearly constrained l1-norm minimization.The ChaCS is simple in implementation and generates secure measurement data.However,existing algorithms cannot efficiently solve the nonlinearly constrained l1-norm minimization,and the reconstruction quality is affected by the algorithm-induced parameters.By linearizing nonlinear constraints,this paper proposes to solve the problem by iterative second order cone programming through the transformation of the nonlinearly constrained l1-norm minimization into a series of second order cone programming.The resulting algorithm eliminates the influence of the induced parameter in signal reconstruction.It is convergent and improves the reconstruction performance of signals.The Henon system is taken as examples to expose the estimation performance of frequency sparse signals.Numerical simulations illustrate the effectiveness of the proposed method.
出处
《信号处理》
CSCD
北大核心
2012年第6期806-811,共6页
Journal of Signal Processing
基金
国家自然科学基金资助项目(60971090
61171166
61101193)
关键词
混沌压缩感知
重构
线性化
参数估计
Chaotic Compressive Sensing
Reconstruction
Linearization
Parameter Estimation
