{"title":"一种移动位置估计的协同定位模型","authors":"P. Deng, Lin Liu, P. Fan","doi":"10.1109/PDCAT.2003.1236323","DOIUrl":null,"url":null,"abstract":"In cellular networks, several TDOA (time difference of arrival) location algorithms can be applied to mobile position estimation, however, each algorithm has its own limitations and none of them is proved to be the most reliable one under different channel environments. Kleine-Ostmann's data fusion model [T. Kleine-Ostmann et al. (2001)] is modified and a collaborative location model which incorporate position estimate of two major TDOA location algorithms is proposed. It is shown by analysis and simulation that more reliable and accurate estimated mobile position can be achieved based on this model.","PeriodicalId":145111,"journal":{"name":"Proceedings of the Fourth International Conference on Parallel and Distributed Computing, Applications and Technologies","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A collaborative location model for mobile position estimation\",\"authors\":\"P. Deng, Lin Liu, P. Fan\",\"doi\":\"10.1109/PDCAT.2003.1236323\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In cellular networks, several TDOA (time difference of arrival) location algorithms can be applied to mobile position estimation, however, each algorithm has its own limitations and none of them is proved to be the most reliable one under different channel environments. Kleine-Ostmann's data fusion model [T. Kleine-Ostmann et al. (2001)] is modified and a collaborative location model which incorporate position estimate of two major TDOA location algorithms is proposed. It is shown by analysis and simulation that more reliable and accurate estimated mobile position can be achieved based on this model.\",\"PeriodicalId\":145111,\"journal\":{\"name\":\"Proceedings of the Fourth International Conference on Parallel and Distributed Computing, Applications and Technologies\",\"volume\":\"47 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-10-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Fourth International Conference on Parallel and Distributed Computing, Applications and Technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PDCAT.2003.1236323\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Fourth International Conference on Parallel and Distributed Computing, Applications and Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PDCAT.2003.1236323","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
在蜂窝网络中,有几种TDOA(到达时差)定位算法可用于移动位置估计,但每种算法都有其局限性,在不同信道环境下,没有一种算法被证明是最可靠的。Kleine-Ostmann数据融合模型[j]。对Kleine-Ostmann et al.(2001)]进行了修正,提出了一种结合两种主要TDOA定位算法的位置估计的协同定位模型。分析和仿真结果表明,基于该模型可以获得更可靠、更准确的移动位置估计。
A collaborative location model for mobile position estimation
In cellular networks, several TDOA (time difference of arrival) location algorithms can be applied to mobile position estimation, however, each algorithm has its own limitations and none of them is proved to be the most reliable one under different channel environments. Kleine-Ostmann's data fusion model [T. Kleine-Ostmann et al. (2001)] is modified and a collaborative location model which incorporate position estimate of two major TDOA location algorithms is proposed. It is shown by analysis and simulation that more reliable and accurate estimated mobile position can be achieved based on this model.