Introduction Adaptive filter concept By means of the known filter parameters of the previous time,update the filter parameters of the 9( current time to be suitable to the e unknown statistical properties of signals a and noise for the optimum filter. 6
6 Introduction Adaptive filter concept By means of the known filter parameters of the previous time, update the filter parameters of the current time to be suitable to the unknown statistical properties of signals and noise for the optimum filter
Several main adaptive filters (1)LMS adaptive filter(闭环结构) (2)RLS (Recursive least-squares) adaptive filter （开环结构） (3)IIR adaptive filter ■■■■■ 7
7 Several main adaptive filters (1) LMS adaptive filter ( 闭 环 结 构 ） (2) RLS (Recursive least – squares) adaptive filter （ 开 环 结 构 ） (3) IIR adaptive filter 2222
Main applications of AWF Adaptive Noise Canceling Adaptive line enhance 8
8 Main applications of AWF Adaptive Noise Canceling Adaptive line enhance
LMS adaptive Wiener filter The LMS adaptive Wiener filter consists of two basic processes: (1)A filtering process (a.input-output; b.an estimation error)Wiener filtering (2)An adaptive process (the automatic adjustment of the parameters of the filter in accordance with the estimation error) 9
9 LMS adaptive Wiener filter The LMS adaptive Wiener filter consists of two basic processes: (1) A filtering process (a. input–output; b. an estimation error) Wiener filtering (2) An adaptive process (the automatic adjustment of the parameters of the filter in accordance with the estimation error)
LMS adaptive Wiener filter 1 Filtering processing (Wiener filter) Adaptive linear components: x(k-1) 砀 x(k-2 () : x(-0 E() d() 10
10 LMS adaptive Wiener filter Adaptive linear components: 1 Filtering processing （Wiener filter)