{"title":"On the performance of the MLE of adaptive array weights: a comparison","authors":"Shen-De Lin","doi":"10.1109/PACRIM.1989.48397","DOIUrl":null,"url":null,"abstract":"The maximum-likelihood (ML) adaptive weight vector is derived, and its performance is compared with those of the sample matrix inversion (SMI) method and the least-mean-square (LMS) algorithm. The ML adaptive weight vector and the SMI method achieve identical convergence rates for the average SNR. With the desired signal absent, they are superior to the LMS algorithm. However, when the desired signal is present and the optimum SNR which depends on the received SNR is large, they lose their superiority. For the average MSE performance, the convergence of the ML adaptive weights is the fastest when the optimum SNR is high enough. With a small optimum SNR, the SMI method performs better than the other algorithms.<<ETX>>","PeriodicalId":256287,"journal":{"name":"Conference Proceeding IEEE Pacific Rim Conference on Communications, Computers and Signal Processing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1989-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Conference Proceeding IEEE Pacific Rim Conference on Communications, Computers and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PACRIM.1989.48397","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The maximum-likelihood (ML) adaptive weight vector is derived, and its performance is compared with those of the sample matrix inversion (SMI) method and the least-mean-square (LMS) algorithm. The ML adaptive weight vector and the SMI method achieve identical convergence rates for the average SNR. With the desired signal absent, they are superior to the LMS algorithm. However, when the desired signal is present and the optimum SNR which depends on the received SNR is large, they lose their superiority. For the average MSE performance, the convergence of the ML adaptive weights is the fastest when the optimum SNR is high enough. With a small optimum SNR, the SMI method performs better than the other algorithms.<>
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
