基于混沌压缩感知的稀疏时变信号在线估计

混沌压缩感知是一种利用混沌系统实现非线性测量的压缩感知理论。针对稀疏时变信号的混沌压缩感知，该文提出稀疏时变信号的在线估计架构，构建一种递归最小二乘准则下的稀疏约束目标函数；通过利用迭代加权非线性最小二乘算法求解目标函数最小化问题，实现稀疏时变信号的参数估计。以Henon混沌系统为例仿真分析了频域时变稀疏信号的估计性能，数值模拟证明了该方法的有效性。

Online Estimation of Sparse Time-varying Signals with Chaotic Compressive Sensing

Chaotic Compressive Sensing(ChaCS) is a nonlinear compressive sensing approach using chaos systems. This paper extends the ChaCS to perform the online estimation of sparse time-varying signals.An online estimation structure is proposed and a sparsity-constrained recursive least-squares objective function is formulated. The sparse time-varying signals are estimated through iterative reweighted nonlinear least-square algorithm by minimizing the objective function.The Henon system is taken as examples to expose the estimation performance of frequency sparse time-varying signals.Numerical simulations illustrate the effectiveness of the proposed method.