{"title":"多连续测量偏差估计的GLR算法","authors":"G. Lundin, P. Mouyon, A. Manecy","doi":"10.1109/RED-UAS.2017.8101675","DOIUrl":null,"url":null,"abstract":"This paper proposes an algorithm for handling multiple consecutive measurement biases that appear and disappear frequently, typically encountered in GNSS in small low flying UAVs due to range errors and signal blocking. The method is derived as an extension to the well known GLR algorithm and is based on a corrected innovation sequence for detection and an identification stage based on least square estimation. A recursive (RLS) and a non-recursive (LS) solution is proposed in the identification stage. Results in a GNSS position error example show that the proposed algorithms are significantly better than the original algorithm in terms of estimation precision when biases appear and disappear frequently.","PeriodicalId":299104,"journal":{"name":"2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A GLR algorithm for multiple consecutive measurement bias estimation\",\"authors\":\"G. Lundin, P. Mouyon, A. Manecy\",\"doi\":\"10.1109/RED-UAS.2017.8101675\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes an algorithm for handling multiple consecutive measurement biases that appear and disappear frequently, typically encountered in GNSS in small low flying UAVs due to range errors and signal blocking. The method is derived as an extension to the well known GLR algorithm and is based on a corrected innovation sequence for detection and an identification stage based on least square estimation. A recursive (RLS) and a non-recursive (LS) solution is proposed in the identification stage. Results in a GNSS position error example show that the proposed algorithms are significantly better than the original algorithm in terms of estimation precision when biases appear and disappear frequently.\",\"PeriodicalId\":299104,\"journal\":{\"name\":\"2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RED-UAS.2017.8101675\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RED-UAS.2017.8101675","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A GLR algorithm for multiple consecutive measurement bias estimation
This paper proposes an algorithm for handling multiple consecutive measurement biases that appear and disappear frequently, typically encountered in GNSS in small low flying UAVs due to range errors and signal blocking. The method is derived as an extension to the well known GLR algorithm and is based on a corrected innovation sequence for detection and an identification stage based on least square estimation. A recursive (RLS) and a non-recursive (LS) solution is proposed in the identification stage. Results in a GNSS position error example show that the proposed algorithms are significantly better than the original algorithm in terms of estimation precision when biases appear and disappear frequently.