Robust Kernel-Based Machine Learning Localization Using NLOS TOAs or TDOAs

被引：0

作者：

Li, Jun ^{[1
]}

Lu, I-Tai ^{[1
]}

Lu, Jonathan S. ^{[2
]}

Zhang, Lingwen ^{[3
]}

机构：

[1] NYU, Dept Elect & Comp Engn, Tandon Sch Engn, New York, NY 10003 USA

[2] Polaris Wireless, Mountain View, CA USA

[3] Beijing Jiao Tong Univ, Inst Broadband Wireless Commun, Beijing, Peoples R China

来源：

2017 IEEE LONG ISLAND SYSTEMS, APPLICATIONS AND TECHNOLOGY CONFERENCE (LISAT) | 2017年

关键词：

Localization; TOA; TDOA; NLOS; Kernel-based Machine Learning; fingerprinting; SENSOR NETWORK LOCALIZATION;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A robust kernel-based machine learning localization scheme using time of arrival (TOA) or time difference of arrival (TDOA) in none-line-of-sight (NLOS) environments is proposed. The scheme can provide accurate position estimation while the reference nodes are coarsely and randomly distributed in the area of interests. Moreover, the scheme is insensitive with respect to random TOA synchronization and measurement errors.

引用

页数：6

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