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为了提高稀疏信号贪婪算法的重构性能,提出了一种改进的贪婪重构算法,即稀疏度估计变步长匹配追踪算法.与现有的贪婪算法相比,该算法用约束等距常数和变步长分别来进行稀疏度估计和减少重构所需的时间.通过稀疏度估计,在重构的开始阶段得到估计的稀疏度和支撑集作为初始值,为信号重构提供了初始的稀疏信息.然后,根据初始值计算相关值以及残差,通过回溯思想和可变步长更新上一次迭代得到的支撑集.最后,当满足算法终止条件时,得到正确的信号支撑集,从而准确地重构出原始信号.仿真结果证明,针对稀疏信号重构,所提出的算法提高了重构性能,所需要的运算时间较之前的算法大幅减少.
In order to improve the reconstruction performance of sparse signal greedy algorithm, an improved greedy reconstruction algorithm is proposed, which is called sparseness estimation and variable step matching pursuit algorithm. Compared with the existing greedy algorithm, And variable step size respectively to estimate the sparsity and reduce the time needed for reconstruction.Using sparseness estimation, the estimated sparsity and support set are obtained as the initial values at the beginning of reconstruction to provide the initial sparsity Then, the correlation values and the residuals are calculated based on the initial values, and the support set obtained by the previous iteration is updated by the backtracking idea and the variable steps.Finally, when the algorithm termination condition is satisfied, the correct signal support set is obtained, The original signal is reconstructed.The simulation results show that the proposed algorithm improves the reconstruction performance for sparse signal reconstruction, and the required computation time is greatly reduced compared with the previous algorithm.