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Convex Euclidean distance embedding for collaborative position localization with NLOS mitigation

文献类型：期刊论文

作者Ding, Chao1; Qi, Hou-Duo2
刊名COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
出版日期2017
卷号66期号:1页码:187-218
关键词Euclidean distance matrix Collaborative localization Non-line of sight (NLOS) Augmented Lagrangian Alternating direction method of multipliers (ADMM)
ISSN号0926-6003
DOI10.1007/s10589-016-9858-5
英文摘要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.
资助项目Engineering and Physical Science Research Council (UK)[EP/K007645/1]
WOS研究方向Operations Research & Management Science ; Mathematics
语种英语
WOS记录号WOS:000391453500007
出版者SPRINGER
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/24473]  
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.

入库方式： OAI收割

来源：数学与系统科学研究院

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