{"title":"Optimal configuration analysis for range-only target localization with uncertain sensor positions","authors":"Yi Hou, Ning Hao, Fenghua He, Xinran Zhang","doi":"10.1016/j.sysconle.2024.105863","DOIUrl":null,"url":null,"abstract":"<div><p>The relative sensor-target geometries are of significance in the context of cooperative localization involving multiple sensors working together to localize a target. The optimality analysis of sensor-target geometries is commonly conducted assuming precise knowledge of the sensors’ positions. However, this is not the case in practice due to various uncertainties such as sensor drift, environmental factors, and measurement errors. In this paper, we address the issue of uncertain positions of range-only sensors. Specifically, we consider randomized positions for the sensors, modeled by a Gaussian probability density function. Consequently, the parameters to be estimated become hybrid, comprising both randomized (the sensors’ positions) and non-randomized (the target’s position) elements. A hybrid CRLB is proposed as a measure to characterize the estimation performance of the localization problem under consideration. To efficiently calculate the hybrid CRLB, we derive an approximation and quantify the corresponding error. Furthermore, we determine the optimality condition of sensor-target geometries for range-only localization. A gradient-based algorithm is designed to facilitate the optimization process. The analytical findings are verified through simulations.</p></div>","PeriodicalId":49450,"journal":{"name":"Systems & Control Letters","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems & Control Letters","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167691124001518","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
The relative sensor-target geometries are of significance in the context of cooperative localization involving multiple sensors working together to localize a target. The optimality analysis of sensor-target geometries is commonly conducted assuming precise knowledge of the sensors’ positions. However, this is not the case in practice due to various uncertainties such as sensor drift, environmental factors, and measurement errors. In this paper, we address the issue of uncertain positions of range-only sensors. Specifically, we consider randomized positions for the sensors, modeled by a Gaussian probability density function. Consequently, the parameters to be estimated become hybrid, comprising both randomized (the sensors’ positions) and non-randomized (the target’s position) elements. A hybrid CRLB is proposed as a measure to characterize the estimation performance of the localization problem under consideration. To efficiently calculate the hybrid CRLB, we derive an approximation and quantify the corresponding error. Furthermore, we determine the optimality condition of sensor-target geometries for range-only localization. A gradient-based algorithm is designed to facilitate the optimization process. The analytical findings are verified through simulations.
期刊介绍:
Founded in 1981 by two of the pre-eminent control theorists, Roger Brockett and Jan Willems, Systems & Control Letters is one of the leading journals in the field of control theory. The aim of the journal is to allow dissemination of relatively concise but highly original contributions whose high initial quality enables a relatively rapid review process. All aspects of the fields of systems and control are covered, especially mathematically-oriented and theoretical papers that have a clear relevance to engineering, physical and biological sciences, and even economics. Application-oriented papers with sophisticated and rigorous mathematical elements are also welcome.