p-范数似约束泄漏LMS算法用于稀疏系统识别
p-norm-like Constraint Leaky LMS Algorithm for Sparse System Identification
AI总结：
本文提出一种基于p-范数约束的泄漏LMS算法，用于稀疏系统识别，通过在成本函数中引入p-范数惩罚项，提升权重更新的收缩效果，从而在噪声输入下改善稀疏系统识别性能。
摘要：
本文提出了一种新颖的泄漏最小均方（leaky LMS, LLMS）算法，该算法采用p-范数似约束来迫使解在系统识别应用中保持稀疏性。作为LMS算法的扩展，LLMS算法因标准LMS算法在高相关输入下的性能下降而被提出。然而，两者均未考虑稀疏性信息以获得更优表现。作为LLMS的稀疏感知改进，所提出的Lplike-LLMS算法将p-范数似惩罚纳入LLMS的成本函数中，从而在权重更新中实现收缩，进而在稀疏系统识别设置中提升性能。仿真结果表明，所提算法在存在噪声输入信号的情况下，能提升滤波器在稀疏系统设置中的性能。
英文摘要：
In this paper, we propose a novel leaky least mean square (leaky LMS, LLMS) algorithm which employs a p-norm-like constraint to force the solution to be sparse in the application of system identification. As an extension of the LMS algorithm which is the most widely-used adaptive filtering technique, the LLMS algorithm has been proposed for decades, due to the deteriorated performance of the standard LMS algorithm with highly correlated input. However, both ofthem do not consider the sparsity information to have better behaviors. As a sparse-aware modification of the LLMS, our proposed Lplike-LLMS algorithm, incorporates a p-norm-like penalty into the cost function of the LLMS to obtain a shrinkage in the weight update, which then enhances the performance in sparse system identification settings. The simulation results show that the proposed algorithm improves the performance of the filter in sparse system settings in the presence of noisy input signals.
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