{"title":"基于到达时间的定位的全局优化","authors":"Michael Pauley, J. Manton","doi":"10.1109/SSP.2018.8450751","DOIUrl":null,"url":null,"abstract":"Synchronous and asynchronous time of arrival-based localisation problems are considered. The likelihood functions in these problems are non-convex and can have issues of local extrema. Typical approaches therefore approximate maximum likelihood estimation by something easier to compute. We aim for global optimisation with guarantees, which we achieve by partitioning the search space into regions, each containing at most one critical point.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"437 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Global Optimisation for Time of Arrival-Based Localisation\",\"authors\":\"Michael Pauley, J. Manton\",\"doi\":\"10.1109/SSP.2018.8450751\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Synchronous and asynchronous time of arrival-based localisation problems are considered. The likelihood functions in these problems are non-convex and can have issues of local extrema. Typical approaches therefore approximate maximum likelihood estimation by something easier to compute. We aim for global optimisation with guarantees, which we achieve by partitioning the search space into regions, each containing at most one critical point.\",\"PeriodicalId\":330528,\"journal\":{\"name\":\"2018 IEEE Statistical Signal Processing Workshop (SSP)\",\"volume\":\"437 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE Statistical Signal Processing Workshop (SSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SSP.2018.8450751\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Statistical Signal Processing Workshop (SSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSP.2018.8450751","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Global Optimisation for Time of Arrival-Based Localisation
Synchronous and asynchronous time of arrival-based localisation problems are considered. The likelihood functions in these problems are non-convex and can have issues of local extrema. Typical approaches therefore approximate maximum likelihood estimation by something easier to compute. We aim for global optimisation with guarantees, which we achieve by partitioning the search space into regions, each containing at most one critical point.