Soft Range Information for Network Localization
Soft Range Information for Network Localization
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DOI：
10.1109/tsp.2018.2795537
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发表时间：
2018-06-15
影响因子：
5.4
通讯作者：
Win, Moe Z.
中科院分区：
文献类型：
--
作者：
Mazuelas, Santiago;Conti, Andrea;Win, Moe Z.
尽管从测量中提取位置信息的难度很大，但在复杂环境中对精确定位的需求仍在不断增加。传统的基于距离的定位方法依赖于从测量获得的距离估计（例如，接收波形的延迟或强度）。本文更进一步，开发了依赖于所有可能的距离值而不是每个距离的单个估计的定位技术。特别是，软距离信息（SRI）的概念介绍，显示其网络定位的重要作用。然后，我们建立了一个通用的框架，基于SRI的本地化和开发算法，获得SRI使用机器学习技术。所提出的方法的性能是量化的，通过在室内环境中的网络实验。结果表明，基于SRI的定位技术在恶劣的无线环境下，定位性能接近Cramer-Rao下界，明显优于传统定位技术。
Thedemand for accurate localization in complex environments continues to increase despite the difficulty in extracting positional information from measurements. Conventional range-based localization approaches rely on distance estimates obtained from measurements (e.g., delay or strength of received waveforms). This paper goes one step further and develops localization techniques that rely on all probable range values rather than on a single estimate of each distance. In particular, the concept of soft range information (SRI) is introduced, showing its essential role for network localization. We then establish a general framework for SRI-based localization and develop algorithms for obtaining the SRI using machine learning techniques. The performance of the proposed approach is quantified via network experimentation in indoor environments. The results show that SRI-based localization techniques can achieve performance approaching the Cramer-Rao lower bound and significantly outperform the conventional techniques especially in harsh wireless environments.