基于稀疏重构的混叠脉冲序列的周期估计

针对于以往方法在克服抖动大和缺失率高的混叠脉冲信号周期估计上的不足，提出了一种基于稀疏重构的混叠脉冲序列的隐藏子周期估计新方法。该方法首先针对由多个具有不同周期的脉冲串混合而成脉冲序列进行插值重采样，然后利用由Ramanujan构造的周期字典，建立了混叠脉冲序列的稀疏表示模型，最后采用联合l_2，0混合范数算法求得混叠脉冲序列的周期估计。方法的优点是具有较强抗噪能力和抗脉冲缺失能力，并且不受初相的改变影响。所需要较少的脉冲数据长度就能得到准确的周期稀疏解。仿真实验表明，所提方法具有更好的估计性能。

Period estimation of aliasing pulse sequences based on sparse reconstruction

Previous methods have the shortcomings in overcoming the period estimation of aliased pulse signals with large jitter and high missing rate. This paper proposes a new method for hidden sub-period estimation of aliased pulse sequences based on sparse reconstruction. The method first interpolates and re-samples the pulse sequence obtained by mixing multiple pulse trains with different periods, and then establishes the sparse representation model of the aliasing pulse sequence by using the periodic dictionary constructed by Ramanujan. Finally, the joint l_2,0 mixing norm algorithm is applied to obtain the periodic estimation of the aliasing pulse sequence. The method has the advantages of strong anti-noise ability and anti-pulse ability, and is not affected by the change of primary phase. Shorter pulse data length is needed to obtain an accurate periodic sparse solution. Simulation results show that the proposed method has better estimation performance.