ER-CRLB: An Extended Recursive Cramer-Rao Lower Bound Fundamental Analysis Method for Indoor Localization Systems.

	ER-CRLB: An Extended Recursive Cramer-Rao Lower Bound Fundamental Analysis Method for Indoor Localization Systems.
	Zhao, Yubin; Fan, Xiaopeng; Xu, Cheng-Zhong; Li, Xiaofan
刊名	IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
	2017
文献子类	期刊论文
英文摘要	We propose an extended recursive Cramer-Rao lower bound (ER-CRLB) method as a fundamental tool to analyze the performance of wireless indoorlocalization systems. The ER-CRLB fully models the complicated indoor environment, e.g., the sequential position state propagation, the target-anchor geometry effect, the non-line-of-sight (NLOS) identification, and the related prior information. First, we use an abstract function to represent the entire wireless localization system model. Then, the unknown vector of the ER-CRLB consists of two parts: The first part is the estimated vector, and the second part is the auxiliary vector that helps improve the estimation accuracy. Accordingly, the Fisher information matrix (FIM) of the ER-CRLB is divided into two parts, namely, the state matrix and the auxiliary matrix. Based on this idea, ER-CRLB can be a practical fundamental limit to denote the system which fuses multiple information in the complicated environment, e.g., recursive Bayesian estimation based on the hidden Markov model, the map matching method and NLOS identification and mitigation methods. When only a small set of unknown vectors is estimated in the system, the ER-CRLB is equivalent to the other CRLBs of the wireless indoor localization system as well. However, the ER-CRLB is more adaptable than other CRLBs when considering more unknown important factors. We employ the ER-CRLB to analyze the time-of-arrival (TOA) range-based indoor localization system. The influence of the hybrid line-of-sight (LOS)/NLOS channels, the building layout information, and the relative height differences between the target and anchors are analyzed. It is demonstrated that the ER-CRLB exploits all the available information for the indoor localization systems and serves as a fundamental limit of the unbiased estimation accuracy.
URL标识	查看原文
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/12515]
专题	深圳先进技术研究院_数字所
作者单位	IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
推荐引用方式 GB/T 7714	Zhao, Yubin,Fan, Xiaopeng,Xu, Cheng-Zhong,et al. ER-CRLB: An Extended Recursive Cramer-Rao Lower Bound Fundamental Analysis Method for Indoor Localization Systems.[J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,2017.
APA	Zhao, Yubin,Fan, Xiaopeng,Xu, Cheng-Zhong,&Li, Xiaofan.(2017).ER-CRLB: An Extended Recursive Cramer-Rao Lower Bound Fundamental Analysis Method for Indoor Localization Systems..IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY.
MLA	Zhao, Yubin,et al."ER-CRLB: An Extended Recursive Cramer-Rao Lower Bound Fundamental Analysis Method for Indoor Localization Systems.".IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2017).