混沌模拟信息转换——基于多射法的稀疏信号重构

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。

Chaotic Analog-to-information Conversion: Sparse Signal Reconstruction with Multiple Shooting Method

Chaotic Compressive Sensing (CS) is a nonlinear compressive sensing theory which utilizes the 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-Squares (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.