NSSC OpenIR
Novel Adaptive Filtering Algorithms Based on Higher-Order Statistics and Geometric Algebra
Alternative TitleWOS:000530830800026;20202108675463
He, Yinmei; Wang, Rui; Wang, Xiangyang; Zhou, Jian1; Yan, Yi2
Source PublicationIEEE ACCESS
2020
Volume8Pages:73767-73779
DOI10.1109/ACCESS.2020.2988521
ISSN2169-3536
Language英语
KeywordSignal processing algorithms Cost function Algebra Higher order statistics Convergence Manganese Signal processing Adaptive filters geometric algebra least-mean fourth least-mean mixed-norm MEAN 4TH ALGORITHM IMAGE
AbstractAdaptive filtering algorithms based on higher-order statistics are proposed for multi-dimensional signal processing in geometric algebra (GA) space. In this paper, the proposed adaptive filtering algorithms utilize the advantage of GA theory in multi-dimensional signal processing to represent a multi-dimensional signal as a GA multivector. In addition, the original least-mean fourth (LMF) and least-mean mixed-norm (LMMN) adaptive filtering algorithms are extended to GA space for multi-dimensional signal processing. Both the proposed GA-based least-mean fourth (GA-LMF) and GA-based least-mean mixed-norm (GA-LMMN) algorithms need to minimize cost functions based on higher-order statistics of the error signal in GA space. The simulation results show that the proposed GA-LMF algorithm performs better in terms of convergence rate and steady-state error under a much smaller step size. The proposed GA-LMMN algorithm makes up for the instability of GA-LMF as the step size increases, and its performance is more stable in mean absolute error and convergence rate.
Indexed BySCI ; EI
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Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/7690
Collection中国科学院国家空间科学中心
Affiliation1.Shanghai Univ, Key Lab Specialty Fiber Opt & Opt Access Networks, Joint Int Res Lab Specialty Fiber Opt & Adv Commu, Sch Commun & Informat Engn,Shanghai Inst Adv Comm, Shanghai 200444, Peoples R China
2.Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Key Lab Terahertz Solid State Technol, Shanghai 200050, Peoples R China
3.Chinese Acad Sci, Natl Space Sci Ctr, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
He, Yinmei,Wang, Rui,Wang, Xiangyang,et al. Novel Adaptive Filtering Algorithms Based on Higher-Order Statistics and Geometric Algebra[J]. IEEE ACCESS,2020,8:73767-73779.
APA He, Yinmei,Wang, Rui,Wang, Xiangyang,Zhou, Jian,&Yan, Yi.(2020).Novel Adaptive Filtering Algorithms Based on Higher-Order Statistics and Geometric Algebra.IEEE ACCESS,8,73767-73779.
MLA He, Yinmei,et al."Novel Adaptive Filtering Algorithms Based on Higher-Order Statistics and Geometric Algebra".IEEE ACCESS 8(2020):73767-73779.
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