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中文摘要：
该文研究和改进了l0-LMS算法以提高对稀疏系统进行辨识的性能。首先依据均方误差反映出的收敛深度信息动态调节步长,提高了算法的收敛速度;其次利用估计误差绝对值加权修正零吸引函数,减小了稳态失调误差。然后定性分析了改进算法中各个参数的取值对收敛速度和稳态性能的影响。最后,计算机仿真验证了新算法的性能明显优于原l0-LMS算法和若干现有稀疏系统辨识的方法。
英文摘要：
In order to improve the performance of sparse system identification,the l0 norm constraint LMS algorithm is studied and improved in this paper.Firstly,the convergence of the algorithm is accelerated by the introduction of a step size control method based on the status information provided by mean square estimation error.Secondly,the zero attraction item is reweighted by the absolute estimation error to reduce the steady-state misalignment.Then the parameters in the proposed algorithm,which control the convergence and steady-state misalignment,are discussed qualitatively.Finally,the simulations demonstrate that the proposed algorithm significantly outperforms l0-LMS and several other existing sparse system identification algorithms.
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同项目期刊论文
A Stochastic Gradient Approach on Compressive Sensing Signal Reconstruction Based on Adaptive Filter
Proof of convergence and performance analysis for sparse recovery via zero-point attracting projecti
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