中文摘要：

使用有网格的观测字典进行稀疏信道估计是近年来常用的多径稀疏信道估计方法，而网格的存在使得这种方法存在估计精度较差的问题，尤其在网格间距较大时，这种方法的劣势更加明显。本文针对这个问题，抛弃了传统的观测字典，基于连续压缩感知理论，构建出可以施加原子范数最优化的原子集，提出了更加精确的多径稀疏信道估计方法。这种方法避免了网格化带来的误差，实现了高精度、超分辨率的估计。本文首先对此进行了理论阐述，进而在两种不同的多径稀疏信道模型下进行了仿真试验，并从估计精度、计算效率等方面与其他有网格稀疏估计方法以及去网格估计方法进行了对比。仿真结果证明，采用本文提出的方法进行多径稀疏信道估计时，相比其他算法可以更加精确地估计出信道冲激响应。

英文摘要：

Sparse recovery using a measuring matrix with grid is a common way to estimate muhipath sparse channel. Grid is the main source of error in this kind of methods. The drawbacks of these methods could be significant when the grid inter- val is large. A much more accurate estimating method is proposed in this paper employing the theory of continuous compressed sensing. This method abandons the dictionary and grid thus avoids the error that is caused by grid, so that it could estimate the muhipath sparse channel with high accuracy and resolution. This paper describes the method theoretically and then testi- fies it in two different sparse channel models. Numerical simulation results illustrate that the proposed method could estimate the multipath sparse channel very accurately and it outperforms the conventional methods that are with grid or off grid.