{"title":"LFM signal parameters estimation using an improved DIRECT algorithm","authors":"Dan Ding, Naiping Cheng","doi":"10.1109/ICIST.2014.6920326","DOIUrl":null,"url":null,"abstract":"In the paper, the widely used numerical optimization method for linear frequency modulated (LFM) signal parameters estimation is modified. To this purpose, an improved Dividing RECTangles (DIRECT) algorithm is proposed to substitute for the commonly used grid search method. The proposed global optimization algorithm can provide initial estimates for local optimization algorithms such as Newton and Simplex. Based on classical DIRECT algorithm, the improved version, called Lipschitz constant assisted DIRECT (L-DIRECT) Algorithm, eliminates hopeless areas, suspends unlikely areas, and concentrates on more promising areas in search space, finding the range of attraction (ROA) with lower SNR threshold or less computational burden for local optimization algorithms. The effect of the modification is validated by simulation results.","PeriodicalId":306383,"journal":{"name":"2014 4th IEEE International Conference on Information Science and Technology","volume":"273 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 4th IEEE International Conference on Information Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIST.2014.6920326","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
In the paper, the widely used numerical optimization method for linear frequency modulated (LFM) signal parameters estimation is modified. To this purpose, an improved Dividing RECTangles (DIRECT) algorithm is proposed to substitute for the commonly used grid search method. The proposed global optimization algorithm can provide initial estimates for local optimization algorithms such as Newton and Simplex. Based on classical DIRECT algorithm, the improved version, called Lipschitz constant assisted DIRECT (L-DIRECT) Algorithm, eliminates hopeless areas, suspends unlikely areas, and concentrates on more promising areas in search space, finding the range of attraction (ROA) with lower SNR threshold or less computational burden for local optimization algorithms. The effect of the modification is validated by simulation results.