Lognormal Mixture Cramer-Rao Lower Bound for Localization

被引：0
|
作者：
Buyukcorak, Saliha [1 ,2 ]
Kurt, Gunes Karabulut [1 ]
Yongacoglu, Abbas [3 ]
机构：
[1] Istanbul Tech Univ, Fac Elect & Elect Engn, TR-34469 Istanbul, Turkey
[2] Gebze Tech Univ, Fac Elect Engn, TR-41400 Gebze, Kocaeli, Turkey
[3] Univ Ottawa, Sch Elect Engn & Comp Sci, Ottawa, ON, Canada
来源：
2015 INTERNATIONAL WIRELESS COMMUNICATIONS & MOBILE COMPUTING CONFERENCE (IWCMC) | 2015年
关键词：
Lognormal Mixture Model; Cramer-Rao Bounds; Localization;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
In received signal strength (RSS) based localization problems, the accuracy of the position information obtained is closely associated with the RSS model used. Therefore, positioning success can be improved with a more accurate RSS model. In this study, to analyze the effect of RSS model in localization performance, lognormal mixture shadowing model is used that provides a more accurate RSS model than the classical lognormal shadowing model. For the corresponding mixture model, a tight upper bound for Cramer-Rao lower bound (CRLB) based on Jensen's inequality is derived. The obtained expressions are used in CRLB analyses that are employed in determination of the best estimates in localization. The improved localization accuracy with lognormal mixture shadowing model is demonstrated by means of examining various numerical analyses.
引用
收藏
页码：132 / 136
页数：5
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