{"title":"Volterra级数展开的MMD-ARMA近似","authors":"Veit, Ulrich Appel","doi":"10.1109/ACSSC.1998.750867","DOIUrl":null,"url":null,"abstract":"Nonlinear filtering based on the Volterra series expansion is a powerful universal tool in signal processing. Due to the problem of increased complexity for higher orders and filter lengths, approximations up to third order nonlinearities using linear FIR-filters and multipliers have been developed earlier called multimemory decomposition (MMD). In our paper we go a step further in this approach using ARMA-filters instead which leads to a reduction in the number of coefficients to about 50% for similar system functions. The good performance of this new approach is demonstrated by means of a processor designed for identification of nonlinear loudspeaker distortions.","PeriodicalId":393743,"journal":{"name":"Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284)","volume":"147 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MMD-ARMA approximation to the Volterra series expansion\",\"authors\":\"Veit, Ulrich Appel\",\"doi\":\"10.1109/ACSSC.1998.750867\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nonlinear filtering based on the Volterra series expansion is a powerful universal tool in signal processing. Due to the problem of increased complexity for higher orders and filter lengths, approximations up to third order nonlinearities using linear FIR-filters and multipliers have been developed earlier called multimemory decomposition (MMD). In our paper we go a step further in this approach using ARMA-filters instead which leads to a reduction in the number of coefficients to about 50% for similar system functions. The good performance of this new approach is demonstrated by means of a processor designed for identification of nonlinear loudspeaker distortions.\",\"PeriodicalId\":393743,\"journal\":{\"name\":\"Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284)\",\"volume\":\"147 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1998-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACSSC.1998.750867\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACSSC.1998.750867","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
MMD-ARMA approximation to the Volterra series expansion
Nonlinear filtering based on the Volterra series expansion is a powerful universal tool in signal processing. Due to the problem of increased complexity for higher orders and filter lengths, approximations up to third order nonlinearities using linear FIR-filters and multipliers have been developed earlier called multimemory decomposition (MMD). In our paper we go a step further in this approach using ARMA-filters instead which leads to a reduction in the number of coefficients to about 50% for similar system functions. The good performance of this new approach is demonstrated by means of a processor designed for identification of nonlinear loudspeaker distortions.