Global and Asymptotically Efficient Localization From Range Measurements

doi:10.1109/TSP.2022.3198167

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	Global and Asymptotically Efficient Localization From Range Measurements
	Zeng, Guangyang 1,2; Mu, Biqiang 3; Chen, Jiming 1,2; Shi, Zhiguo 4; Wu, Junfeng 2,5
	2022
发表期刊	IEEE TRANSACTIONS ON SIGNAL PROCESSING
ISSN	1053-587X
卷号	70 页码:5041-5057
摘要	We consider the range-based localization problem, which involves estimating an object's position by using m sensors, hoping that as the number m of sensors increases, the estimate converges to the true position with the minimum variance. We show that under some conditions on the sensor deployment and measurement noises, the LS estimator is strongly consistent and asymptotically normal. However, the LS problem is nonsmooth and nonconvex, and therefore hard to solve. We then devise realizable estimators that possess the same asymptotic properties as the LS one. These estimators are based on a two-step estimation architecture, which says that any root m-consistent estimate followed by a one-step Gauss-Newton iteration can yield a solution that possesses the same asymptotic property as the LS one. The keypoint of the two-step scheme is to construct a root m-consistent estimate in the first step. In terms of whether the variance of measurement noises is known or not, we propose the Bias-Eli estimator (which involves solving a generalized trust region subproblem) and the Noise-Est estimator (which is obtained by solving a convex problem), respectively. Both of them are proved to be root m-consistent. Moreover, we show that by discarding the constraints in the above two optimization problems, the resulting closed-form estimators (called Bias-Eli-Lin and Noise-Est-Lin) are also root m-consistent. Plenty of simulations verify the correctness of our theoretical claims, showing that the proposed two-step estimators can asymptotically achieve the Cramer-Rao lower bound.
关键词	Range measurements TOA localization two-step localization large-sample analysis
DOI	10.1109/TSP.2022.3198167
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62003303] ; Guangdong Provincial Key Laboratory of Big Data Computing, The Chinese University of Hong Kong, Shenzhen[B10120210117-KP02] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA27000000]
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000880643100002
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/60659
专题	中国科学院数学与系统科学研究院
通讯作者	Wu, Junfeng
作者单位	1.Zhejiang Univ, Coll Control Sci & Engn, Hangzhou 310027, Peoples R China 2.Zhejiang Univ, State Key Lab Ind Control Technol, Hangzhou 310027, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Inst Syst Sci, Key Lab Syst & Control, Beijing 100190, Peoples R China 4.Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou 310027, Peoples R China 5.Chinese Univ Hong Kong, Sch Data Sci, Shenzhen 518172, Peoples R China
推荐引用方式 GB/T 7714	Zeng, Guangyang,Mu, Biqiang,Chen, Jiming,et al. Global and Asymptotically Efficient Localization From Range Measurements[J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING,2022,70:5041-5057.
APA	Zeng, Guangyang,Mu, Biqiang,Chen, Jiming,Shi, Zhiguo,&Wu, Junfeng.(2022).Global and Asymptotically Efficient Localization From Range Measurements.IEEE TRANSACTIONS ON SIGNAL PROCESSING,70,5041-5057.
MLA	Zeng, Guangyang,et al."Global and Asymptotically Efficient Localization From Range Measurements".IEEE TRANSACTIONS ON SIGNAL PROCESSING 70(2022):5041-5057.