{"title":"Matrix adaptive synthesis filter for uniform filter bank","authors":"Sandeep Patel, R. Dhuli, Brejesh Lall","doi":"10.1109/NCC.2013.6487978","DOIUrl":null,"url":null,"abstract":"In this paper, we use a matrix adaptive filter as the synthesis stage of a Uniform Filter Bank (UFB) to reconstruct the input signal. We first develop the mathematical theory behind it by applying the model of optimal filtering at the synthesis stage of the UFB and obtaining an expression for the matrix Wiener filter. We have developed a theorem which we use to simplify the expression further. In the absence of required information about the analysis stage, we use adaptive filtering to arrive at the Wiener solution. We use the Least Mean Square (LMS) algorithm to update the filter coefficients. Through experimental results, we find that the adaptive filter is convergent for a stable Wiener filter.","PeriodicalId":202526,"journal":{"name":"2013 National Conference on Communications (NCC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 National Conference on Communications (NCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NCC.2013.6487978","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper, we use a matrix adaptive filter as the synthesis stage of a Uniform Filter Bank (UFB) to reconstruct the input signal. We first develop the mathematical theory behind it by applying the model of optimal filtering at the synthesis stage of the UFB and obtaining an expression for the matrix Wiener filter. We have developed a theorem which we use to simplify the expression further. In the absence of required information about the analysis stage, we use adaptive filtering to arrive at the Wiener solution. We use the Least Mean Square (LMS) algorithm to update the filter coefficients. Through experimental results, we find that the adaptive filter is convergent for a stable Wiener filter.
本文采用矩阵自适应滤波器作为均匀滤波器组(Uniform filter Bank, UFB)的合成级来重构输入信号。我们首先通过在UFB合成阶段应用最优滤波模型并获得矩阵维纳滤波器的表达式来发展其背后的数学理论。我们发展了一个定理,用来进一步简化这个表达式。在缺乏分析阶段所需信息的情况下,我们使用自适应滤波来得到维纳解。我们使用最小均方(LMS)算法来更新滤波器系数。实验结果表明,对于稳定的维纳滤波器,自适应滤波器是收敛的。