具有重加权l0 -范数约束的连续混合p-范数自适应算法

2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS) Pub Date : 2017-06-01 DOI:10.1109/EIIS.2017.8298617

Sihai Guan, Zhi Li, Hairu Zhang

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引用次数: 2

摘要

提出了一种带有重加权l0 -范数约束的连续混合p-范数自适应算法(RL0-CMPN)用于稀疏系统辨识。RL0-CMPN算法充分利用了不同范数的优点。该算法解决了大系数更新扩散问题，降低了慢速效应。它是一种连续混合p范数自适应算法。讨论了该算法的计算复杂度。最后，在不同信噪比下，将该算法与现有的一些自适应滤波算法进行了比较。理论分析与实验仿真相结合表明，该算法具有较高的跟踪速度、较低的稳态误差和抗噪声性能。

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Continuous mixed p-norm adaptive algorithm with reweighted L0-norm constraint

A continuous mixed p-norm adaptive algorithm with reweighted L0-norm constraint (RL0-CMPN) is proposed for sparse system identification. The RL0-CMPN algorithm makes full use of the advantages of the different norm. This algorithm can solve large coefficient update spread problem and reduce the slow-down effect. Besides, it is a continuous mixed p-norm adaptive algorithm. The computation complexity of the algorithm is discussed. Finally, the algorithm is compared with some exist adaptive filtering algorithms in different signal-tonoise ratio (SNR). Theoretical analysis combined with experimental simulations show that the algorithm can achieve better tracking speed, lower steady state error and anti-noise performance.

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2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS)

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