自适应环境变化的RSS室内定位方法

doi:10.3969/j.issn.1000-436x.2014.10.024

通信学报 ›› 2014, Vol. 35 ›› Issue (10): 210-217.doi: 10.3969/j.issn.1000-436x.2014.10.024

自适应环境变化的RSS室内定位方法

王婷婷^1,²,柯炜^2,³,孙超²

¹ 南京信息工程大学江苏省气象探测与信息处理重点实验室，江苏南京 210044
² 南京师范大学江苏省光电技术重点实验室，江苏南京 210023
³ 民政部减灾和应急工程重点实验室，北京 100124

出版日期:2014-10-25 发布日期:2017-06-14
基金资助:
国家教育部博士点基金资助项目;国家自然科学基金青年科学基金资助项目;江苏高校优势学科Ⅱ建设工程基金资助项目(“信息与通信工程”优势学科);江苏省高校自然科学研究面上基金资助项目;民政部减灾和应急工程重点实验室开放基金资助项目;气象探测与信息处理重点实验室开放基金资助项目

Environmental-adaptive RSS-based indoor localization

Ting-ting WANG^1,²,Wei KE^2,³,Chao SUN²

¹ Jiangsu Key Laboratory of Meteorological Observation and Information Processing,Nanjing University of Information Science ＆ Technology,Nanjing 210044,China
² Jiangsu Key Lab of Opto-Electronic Technology,Nanjing Normal University,Nanjing 210023,China
³ Key Laboratory of Disaster Reduction and Emergency Response Engineering of the Ministry of Civil Affairs,Beijing 100124,China

Online:2014-10-25 Published:2017-06-14
Supported by:
The Ph.D.Programs Foundation of the Ministry of Education of China;The National Natural Science Foundation of China;The Priority Academic Program Development of Jiangsu Higher Education Institutions;The University Science Research Project of Jiangsu Province;The Open Research Fund of Key Laboratory of Disaster Reduction and Emergency Response Engineering of the Ministry of Civil Affairs;The Open Research Fund of Jiangsu Key Laboratory of Meteorological Observation and Information Processing

摘要/Abstract

摘要：

根据定位问题的天然稀疏性，提出一种基于两步字典学习的定位方法，依据测量值动态调整字典，使稀疏模型能够自适应RSS的变化。同时提出一种改进的加权l₁范数稀疏重构算法，提高低信噪比情况下的重构精度。实验结果表明该方法可以在目标数量未知的情况下实现多目标定位，并具有较强的抗噪声能力。

关键词: 室内定位, 字典学习, 压缩感知, 加权l1范数最小化

Abstract:

A novel two-step dictionary learning (DL) framework was proposed to dynamically adjust the overcomplete basis (a.k.a.dictionary) for matching the changes of the RSS measurements,and then the sparse solution can better represent location estimations.Moreover,a modified re-weighting l₁norm minimization algorithm was proposed to improve reconstruction performance for sparse signals.The effectiveness of the proposed scheme is demonstrated by experimental results where the locations of targets can be obtained from noisy signals,even if the number of targets is not known a priori.

Key words: indoor localization, dictionary learning (DL), compressive sensing

王婷婷,柯炜,孙超. 自适应环境变化的RSS室内定位方法[J]. 通信学报, 2014, 35(10): 210-217.

Ting-ting WANG,Wei KE,Chao SUN. Environmental-adaptive RSS-based indoor localization[J]. Journal on Communications, 2014, 35(10): 210-217.

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算法	RMSE/m	失效率/%	平均耗时/s
对比算法1 s=3 m²	3.91	9	0.610
对比算法2 s=3 m²	4.35	16	0.325
本文算法 s=3 m²	2.77	8	1.211(离线)+0.750(在线)
对比算法1 s=1 m²	2.03	7	0.876
对比算法2 s=1 m²	2.81	12	0.551
本文算法 s=1 m²	1.51	6	2.957(离线)+0.997(在线)

自适应环境变化的RSS室内定位方法

Environmental-adaptive RSS-based indoor localization

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