{"title":"Outlier-Robust Passive Elliptic Target Localization","authors":"Wenxin Xiong, H. So","doi":"10.1109/LGRS.2023.3270929","DOIUrl":null,"url":null,"abstract":"The inadvertent incorporation of deviating samples into the measured indirect and direct path delays is generally unavoidable in the practical implementation of passive elliptic localization. These outlying observations, however, can do great harm to the positioning performance if left untreated. Here, a robust statistics-based method is put forward as the solution to such a problem. The non-outlier-resistant $\\ell _{2}$ cost function in the traditional least squares (LS) formulation is replaced by a certain differentiable error measure that possesses resistance to the presence of abnormally large fitting errors. A globally optimized hybrid quasi-Newton and particle swarm optimization (PSO) algorithm is then developed for an efficient realization of the robust estimator. The strong capability of the presented approach to deal with outliers and its applicability to typical adverse localization environments are demonstrated via simulations.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"20 1","pages":"1-5"},"PeriodicalIF":4.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Geoscience and Remote Sensing Letters","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1109/LGRS.2023.3270929","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 1
Abstract
The inadvertent incorporation of deviating samples into the measured indirect and direct path delays is generally unavoidable in the practical implementation of passive elliptic localization. These outlying observations, however, can do great harm to the positioning performance if left untreated. Here, a robust statistics-based method is put forward as the solution to such a problem. The non-outlier-resistant $\ell _{2}$ cost function in the traditional least squares (LS) formulation is replaced by a certain differentiable error measure that possesses resistance to the presence of abnormally large fitting errors. A globally optimized hybrid quasi-Newton and particle swarm optimization (PSO) algorithm is then developed for an efficient realization of the robust estimator. The strong capability of the presented approach to deal with outliers and its applicability to typical adverse localization environments are demonstrated via simulations.
期刊介绍:
IEEE Geoscience and Remote Sensing Letters (GRSL) is a monthly publication for short papers (maximum length 5 pages) addressing new ideas and formative concepts in remote sensing as well as important new and timely results and concepts. Papers should relate to the theory, concepts and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space, and the processing, interpretation, and dissemination of this information. The technical content of papers must be both new and significant. Experimental data must be complete and include sufficient description of experimental apparatus, methods, and relevant experimental conditions. GRSL encourages the incorporation of "extended objects" or "multimedia" such as animations to enhance the shorter papers.