基于智能手机的精细时间范围测量的评估与校正

IF 1.8 Q3 REMOTE SENSING
Y. Bai, A. Kealy, Lucas Holden
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引用次数: 5

摘要

进入21世纪以来,随着智能手机的快速发展和应用,基于wi - fi的定位技术已被公认为定位服务(LBS)的一项有用而重要的技术。然而,在过去的20年里,没有一种成熟的基于wi - fi的定位技术或方法能够提供令人满意的输出,直到最近,IEEE 802.11mc标准发布并在市场上得到硬件支持,该标准在不涉及接收信号强度指示器(RSSI)的情况下,使用精细时间测量(FTM)协议和多次往返时间(RTT)来实现更精确和鲁棒的测距。提出了一种基于Wi-Fi FTM的测距评估和测距偏移校正方法。通过两个精心设计的评估试验,具体考察了测距偏移误差的特性。此外,还比较了强迫症野生路由器和谷歌接入点(AP)的偏移误差。经过典型的偏移校正过程,从具有视距(LOS)条件的复杂周围环境中获得的距离估计获得了0.181 m的平均精度。该研究成果将成为我们未来研究项目中实现其他算法(如机器学习和多迭代)的有用资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation and correction of smartphone-based fine time range measurements
ABSTRACT Wi-Fi-based positioning technology has been recognised as a useful and important technology for location-based service (LBS) accompanied by the rapid development and application of smartphones since the beginning of the 21st century. However, no mature technology or method of Wi-Fi-based positioning had provided a satisfying output in the past 20 years, until recently, when the IEEE 802.11mc standard was released and hardware-supported in the market, in which a fine time measurement (FTM) protocol and multiple round-trip time (RTT) was used for more accurate and robust ranging without the received signal strength indicator (RSSI) involved. This paper provided an evaluation and ranging offset correction approach for Wi-Fi FTM based ranging. The characteristics of the ranging offset deviation errors are specifically examined through two well-designed evaluation tests. In addition, the offset deviation errors from a CompuLab WILD router and a Google access point (AP) are also compared. An average of 0.181 m accuracy was achieved after a typical offset correction process to the ranging estimates obtained from a complex surrounding environment with line-of-sight (LOS) condition. The research outcome will become a useful resource for implementing other algorithms such as machine learning and multi-lateration for our future research projects.
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来源期刊
CiteScore
5.00
自引率
0.00%
发文量
10
期刊介绍: International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications. Image and data fusion techniques are important for combining the many sources of satellite, airborne and ground based imaging systems, and integrating these with other related data sets for enhanced information extraction and decision making. Image and data fusion aims at the integration of multi-sensor, multi-temporal, multi-resolution and multi-platform image data, together with geospatial data, GIS, in-situ, and other statistical data sets for improved information extraction, as well as to increase the reliability of the information. This leads to more accurate information that provides for robust operational performance, i.e. increased confidence, reduced ambiguity and improved classification enabling evidence based management. The journal welcomes original research papers, review papers, shorter letters, technical articles, book reviews and conference reports in all areas of image and data fusion including, but not limited to, the following aspects and topics: • Automatic registration/geometric aspects of fusing images with different spatial, spectral, temporal resolutions; phase information; or acquired in different modes • Pixel, feature and decision level fusion algorithms and methodologies • Data Assimilation: fusing data with models • Multi-source classification and information extraction • Integration of satellite, airborne and terrestrial sensor systems • Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets (e.g. geometric information, topological information, statistical information, etc.).
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