Set-membership adaptive filtering with parameter-dependent error bound tuning

被引:0
|
作者
Guo, L [1 ]
Huang, YF [1 ]
机构
[1] Univ Notre Dame, Dept Elect Engn, Notre Dame, IN 46556 USA
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper considers set-membership filtering (SMF) when the error-bound specification is hard to determine. Improper error-bound specification could cause overbounding or underbounding, both of which can result in degraded performance for SMF algorithms. This paper introduces a novel variable error bound and presents a different SMF criterion. It is shown that the recursive algorithm derived from this new set-membership filtering criterion has less risk of overbounding/underbounding and outperforms conventional SMF algorithms with one fixed error-bound specification, particularly when insufficient knowledge is available to determine the bound. The proposed algorithm is more suitable to time-variant environments. Frequency-domain equalization for broadband wireless communications is used as an example to illustrate the proposed criterion and recursive solution. Simulation results that show convergence performance and tracking of a time-variant channel are presented.
引用
收藏
页码:369 / 372
页数:4
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