Abstract: A new algorithm is proposed to achieve parameter estimation which is intensively studied and has been widely applied to many areas in signal processing.The idea of Compressed Sensing（CS） theory is introduced into parameter estimation.It integrates the processes of data sampling and compression in tranditional way by means of few non-adaptation random projections using CS algorithm.Compared with Matching Pursuit（MP） algorithm，CS algorithm has the obvious advantage even under sub-Nyquist sample rate.Theoretic analysis and experimental results illustrate the validity of the proposed algorithm.

摘要： 参数估计是信号处理许多领域研究的热点，并有着广泛应用。通过引入压缩传感（Compressed Sensing，CS）理论的思想，提出了一种基于压缩传感理论的信号参数估计方法。它省略了抛弃大部分高速采样的数据来实现压缩的中间过程，通过使用少量非适应随机投影来完成。与匹配追踪（MP）算法相比，此算法在相同的低采样点数下有明显的优势。理论分析及计算机仿真结果证实了算法的有效性。