非高斯环境下基于最大相关熵准则的稀疏自适应滤波信道估计算法
Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-Gaussian environments
摘要：
稀疏自适应信道估计问题因其简单性和鲁棒性而成为宽带无线通信系统中的重要研究课题之一。到目前为止，许多稀疏感知的信道估计算法已经开发的基础上著名的最小均方误差（MMSE）准则，如零吸引最小均方（ZALMS），这是高斯假设下的鲁棒性。然而，在非高斯环境中，这些方法往往不再是鲁棒的，特别是当系统受到随机脉冲噪声干扰时。为了解决这个问题，我们在这项工作中提出了一个强大的稀疏自适应滤波算法，使用相关熵诱导度量（CIM）惩罚最大相关熵准则（MCC），而不是传统的MMSE准则的鲁棒信道估计。具体地说，MCC被用来减轻脉冲噪声，而CIM被采用，以有效地利用信道稀疏。理论分析和计算机模拟证实了所提出的方法。
英文摘要：
Sparse adaptive channel estimation problem is one of the most important topics in broadband wireless communications systems due to its simplicity and robustness. So far many sparsity-aware channel estimation algorithms have been developed based on the well-known minimum mean square error (MMSE) criterion, such as the zero-attracting least mean square (ZALMS), which are robust under Gaussian assumption. In non-Gaussian environments, however, these methods are often no longer robust especially when systems are disturbed by random impulsive noises. To address this problem, we propose in this work a robust sparse adaptive filtering algorithm using correntropy induced metric (CIM) penalized maximum correntropy criterion (MCC) rather than conventional MMSE criterion for robust channel estimation. Specifically, MCC is utilized to mitigate the impulsive noise while CIM is adopted to exploit the channel sparsity efficiently. Both theoretical analysis and computer simulations are provided to corroborate the proposed methods.
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