中文摘要：

混沌压缩采样是应用混沌系统实现非线性测量的压缩采样理论。该文研究模拟信号的混沌压缩采样-混沌模拟信息转换。该转换通过稀疏信号激励混沌系统,低速采样系统输出实现;信号重构则以混沌系统参数估计理论为基础,通过稀疏正则化的非线性最小二乘问题进行求解。该文将多射法（MS）与迭代再加权非线性最小二乘算法（IRNLS）结合,给出混沌模拟信息转换的MS-IRNLS信号重构算法。文中以Lorenz系统为例,仿真验证了MS-IRNLS算法的重构性能,结果表明方法的有效性。

英文摘要：

Chaotic Compressive Sensing （CS） is a nonlinear compressive sensing theory whicl] utmzes ~ne randomness-like characteristic of chaos systems to measure sparse signals. This paper focuses on the chaotic compressive sensing for the acquisition and reconstruction of analog signals, i.e., Chaotic Analog-to-Information （ChaA2I） converter. ChaA2I generates the low-rate samples by sampling the output of chaotic system excited by the sparse signals, and implements the signal reconstruction by solving the sparsity-regularized nonlinear least squares problem. With the view on chaotic parameter estimation, a highly-efficient reconstruction algorithm （MS-IRNLS） is developed by combing the Multiple Shooting （MS） method with the Iteratively Reweighted Nonlinear Least-Squaxes （IRNLS） algorithm. With the Lorenz system as an example, the paper conducts extensive simulations for the reconstruction performance of MS-IRNLS algorithm. The simulations demonstrate the effectiveness of the proposed ChaA2I.