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题名: Group-Constrained Maximum Correntropy Criterion Algorithms for Estimating Sparse Mix-Noised Channels
作者: Wang, Yanyan; Li, Yingsong; Albu, Felix; Yang, Rui
作者部门: 微波遥感部
通讯作者: Li, YS (reprint author), Harbin Engn Univ, Coll Informat & Commun Engn, Harbin 150001, Heilongjiang, Peoples R China. ; Li, YS (reprint author), Chinese Acad Sci, Natl Space Sci Ctr, Beijing 100190, Peoples R China.
关键词: sparse MCC algorithms ; mixed noise environment ; zero-attracting technique ; norm penalties
刊名: ENTROPY
ISSN号: 1099-4300
出版日期: 2017
卷号: 19, 期号:8, 页码:432
收录类别: SCI
项目资助者: PhD Student Research and Innovation Fund of the Fundamental Research Funds for the Central Universities [HEUGIP201707] ; National Key Research and Development Program of China-Government Corporation Special Program [2016YFE0111100] ; National Science Foundation of China [61571149] ; Science and Technology innovative Talents Foundation of Harbin [2016RAXXJ044] ; Projects for the Selected Returned Overseas Chinese Scholars of Heilongjiang Province ; MOHRSS of China
英文摘要: A group-constrained maximum correntropy criterion (GC-MCC) algorithm is proposed on the basis of the compressive sensing (CS) concept and zero attracting (ZA) techniques and its estimating behavior is verified over sparse multi-path channels. The proposed algorithm is implemented by exerting different norm penalties on the two grouped channel coefficients to improve the channel estimation performance in a mixed noise environment. As a result, a zero attraction term is obtained from the expected l(0) and l(1) penalty techniques. Furthermore, a reweighting factor is adopted and incorporated into the zero-attraction term of the GC-MCC algorithm which is denoted as the reweighted GC-MCC (RGC-MMC) algorithm to enhance the estimation performance. Both the GC-MCC and RGC-MCC algorithms are developed to exploit well the inherent sparseness properties of the sparse multi-path channels due to the expected zero-attraction terms in their iterations. The channel estimation behaviors are discussed and analyzed over sparse channels in mixed Gaussian noise environments. The computer simulation results show that the estimated steady-state error is smaller and the convergence is faster than those of the previously reported MCC and sparse MCC algorithms.
语种: 英语
内容类型: 期刊论文
URI标识: http://ir.nssc.ac.cn/handle/122/6128
Appears in Collections:微波遥感部_期刊论文

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