{"title":"Radar detection in K-distributed clutter plus noise using L-statistics","authors":"J. Ritcey","doi":"10.1109/ACSSC.2017.8335532","DOIUrl":null,"url":null,"abstract":"Detection in long-tailed clutter is a challenging problem. Recently, the Generalized Likelihood Ratio Test (GLRT) in K-distributed clutter plus noise has been addressed. It has been shown that the minimum order-statistic detector works well to sort non-fluctuating point targets from clutter. We extend this work to show that, at little additional computational cost, linear combining of sorted values, L-statistics, can provide some additional performance gains, depending on the Clutter-to-Noise Ratio. Results are given primarily through Monte Carlo simulation.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 51st Asilomar Conference on Signals, Systems, and Computers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACSSC.2017.8335532","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
Detection in long-tailed clutter is a challenging problem. Recently, the Generalized Likelihood Ratio Test (GLRT) in K-distributed clutter plus noise has been addressed. It has been shown that the minimum order-statistic detector works well to sort non-fluctuating point targets from clutter. We extend this work to show that, at little additional computational cost, linear combining of sorted values, L-statistics, can provide some additional performance gains, depending on the Clutter-to-Noise Ratio. Results are given primarily through Monte Carlo simulation.