Convex Euclidean distance embedding for collaborative position localization with NLOS mitigation

doi:10.1007/s10589-016-9858-5

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	Convex Euclidean distance embedding for collaborative position localization with NLOS mitigation
	Ding, Chao1 ; Qi, Hou-Duo 2
	2017
发表期刊	COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
ISSN	0926-6003
卷号	66 期号:1 页码:187-218
摘要	One of the challenging problems in collaborative position localization arises when the distance measurements contain non-line-of-sight (NLOS) biases. Convex optimization has played a major role in modelling such problems and numerical algorithm developments. One of the successful examples is the semi-definite programming (SDP), which translates Euclidean distances into the constraints of positive semidefinite matrices, leading to a large number of constraints in the case of NLOS biases. In this paper, we propose a new convex optimization model that is built upon the concept of Euclidean distance matrix (EDM). The resulting EDM optimization has an advantage that its Lagrangian dual problem is well structured and hence is conducive to algorithm developments. We apply a recently proposed 3-block alternating direction method of multipliers to the dual problem and tested the algorithm on some real as well as simulated data of large scale. In particular, the EDM model significantly outperforms the existing SDP model and several others.
关键词	Euclidean distance matrix Collaborative localization Non-line of sight (NLOS) Augmented Lagrangian Alternating direction method of multipliers (ADMM)
DOI	10.1007/s10589-016-9858-5
语种	英语
资助项目	Engineering and Physical Science Research Council (UK)[EP/K007645/1]
WOS研究方向	Operations Research & Management Science ; Mathematics
WOS类目	Operations Research & Management Science ; Mathematics, Applied
WOS记录号	WOS:000391453500007
出版者	SPRINGER
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/24474
专题	应用数学研究所
通讯作者	Ding, Chao
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Inst Appl Math, Beijing, Peoples R China 2.Univ Southampton, Sch Math, Southampton SO17 1BJ, Hants, England
推荐引用方式 GB/T 7714	Ding, Chao,Qi, Hou-Duo. Convex Euclidean distance embedding for collaborative position localization with NLOS mitigation[J]. COMPUTATIONAL OPTIMIZATION AND APPLICATIONS,2017,66(1):187-218.
APA	Ding, Chao,&Qi, Hou-Duo.(2017).Convex Euclidean distance embedding for collaborative position localization with NLOS mitigation.COMPUTATIONAL OPTIMIZATION AND APPLICATIONS,66(1),187-218.
MLA	Ding, Chao,et al."Convex Euclidean distance embedding for collaborative position localization with NLOS mitigation".COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 66.1(2017):187-218.