基于稀疏系数和多级搜索策略的信号参数估计

参数估计是盲信号处理的关键环节，对后续信号的侦察处理意义重大。针对当前压缩域信号参数估计问题，提出了一种新的信号参数估计算法，利用稀疏系数在不同测量矩阵中相同稀疏字典下位置相同的特点，实现了信号参数估计的区间预判；基于理论分析确定了多级搜索策略的最优级次，实现了稀疏字典原子数目的降低。仿真结果表明，算法在提高信号参数估计精度的同时显著降低了运算复杂度。

Signal parameter estimation based on sparse coefficient and multi-stage search strategy

Estimation of signal parameter is the key procedure in blind signal processing,which is very important for subsequence signal capture analysis. In order to solve the problem of signal parameter estimation in compressed domain,a new algorithm for signal parameter estimation is proposed.According to the characteristics that the sparse coefficient has same location information between different measurement matrix in same sparse dictionary,it realizes the signal parameter interval pre-estimation.It uses the optimal multi-level search strategy to reduce atom numbers in sparse dictionary. Simulations of the linear frequence modulation(LFM) signal show that the algorithm obtains high estimation performance and decreases computational complexity.