{"title":"3-D Separable-Denominator Digital Filters with Minimum L2-Sensitivity and No Overflow Oscillations","authors":"T. Hinamoto, O. I. Omoifo","doi":"10.1109/ISPACS.2006.364754","DOIUrl":null,"url":null,"abstract":"The problem of minimizing an L2-sensitivity measure subject to L2-norm dynamic-range scaling constraints for three-dimensional (3-D) separable-denominator digital filters is formulated. The constrained optimization problem is converted into an unconstrained optimization problem by using linear-algebraic techniques. Next, an efficient quasi-Newton algorithm is applied with closed-form formula for gradient evaluation to solve the unconstrained optimization problem. The optimal filter structure is then constructed by employing the resulting coordinate transformation matrix that minimizes the L2-sensitivity measure subject to the scaling constraints. A numerical example is presented to illustrate the utility of the proposed technique","PeriodicalId":178644,"journal":{"name":"2006 International Symposium on Intelligent Signal Processing and Communications","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 International Symposium on Intelligent Signal Processing and Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPACS.2006.364754","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The problem of minimizing an L2-sensitivity measure subject to L2-norm dynamic-range scaling constraints for three-dimensional (3-D) separable-denominator digital filters is formulated. The constrained optimization problem is converted into an unconstrained optimization problem by using linear-algebraic techniques. Next, an efficient quasi-Newton algorithm is applied with closed-form formula for gradient evaluation to solve the unconstrained optimization problem. The optimal filter structure is then constructed by employing the resulting coordinate transformation matrix that minimizes the L2-sensitivity measure subject to the scaling constraints. A numerical example is presented to illustrate the utility of the proposed technique