{"title":"A variable step-size lmp algorithm for heavy-tailed interference suppression in phased array radar","authors":"Y. R. Zheng, Tiange Shao","doi":"10.1109/AERO.2009.4839472","DOIUrl":null,"url":null,"abstract":"A new variable step-size Least Mean p-norm (VSS-LMP) algorithm is proposed for phased array radar application with space-time adaptive processing to combat heavy-tailed non-Gaussian clutters. The algorithms automatically change the step size according to the estimated p-th and (2p - 2)-th moments of the error, where 1 ≤ p ≤ 2. The algorithm is evaluated via a space-slow-time STAP example and the excess Mean Square Error (MSE) and misadjustment results show that the proposed VSS-LMP converges fast and reaches lower steady-state error than the fixed stepsize LMP. It also provides a better compromise between convergence speed and low steady state error than existing VSS Least Mean Square (LMS) algorithms in both Gaussian and Compound K clutter environments.","PeriodicalId":117250,"journal":{"name":"2009 IEEE Aerospace conference","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE Aerospace conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AERO.2009.4839472","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
A new variable step-size Least Mean p-norm (VSS-LMP) algorithm is proposed for phased array radar application with space-time adaptive processing to combat heavy-tailed non-Gaussian clutters. The algorithms automatically change the step size according to the estimated p-th and (2p - 2)-th moments of the error, where 1 ≤ p ≤ 2. The algorithm is evaluated via a space-slow-time STAP example and the excess Mean Square Error (MSE) and misadjustment results show that the proposed VSS-LMP converges fast and reaches lower steady-state error than the fixed stepsize LMP. It also provides a better compromise between convergence speed and low steady state error than existing VSS Least Mean Square (LMS) algorithms in both Gaussian and Compound K clutter environments.