Knowledge management system National Space Science Center,CAS
| Novel Adaptive Filtering Algorithms Based on Higher-Order Statistics and Geometric Algebra | |
| Alternative Title | WOS:000530830800026;20202108675463 |
| He, Yinmei; Wang, Rui; Wang, Xiangyang; Zhou, Jian1; Yan, Yi2 | |
| Source Publication | IEEE ACCESS
 |
| 2020 | |
| Volume | 8Pages:73767-73779 |
| DOI | 10.1109/ACCESS.2020.2988521 |
| ISSN | 2169-3536 |
| Language | 英语 |
| Keyword | Signal 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 |
| Abstract | Adaptive 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 By | SCI ; EI |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.nssc.ac.cn/handle/122/7690 |
| Collection | 中国科学院国家空间科学中心 |
| Affiliation | 1.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. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment