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Accurate indoor positioning using IEEE 802.11mc round trip time
Pervasive and Mobile Computing ( IF 4.3 ) Pub Date : 2021-05-26 , DOI: 10.1016/j.pmcj.2021.101416
Omar Hashem , Khaled A. Harras , Moustafa Youssef

WiFi time of flight (ToF) measurement has been supported recently by the wireless LAN protocols to improve WiFi localization. Specifically, the IEEE 802.11-2016 standard has a fine-time measurement (FTM) protocol that can be used to measure the WiFi signal round trip time (RTT). In this paper, we present the design and implementation of WiNar, a WiFi RTT-based indoor location determination system that combines the advantages of both fingerprint and ranging-based techniques to overcome the different challenges of indoor environments. Using commercial-off-the-shelf access points and mobile phones, WiNar leverages both the propagation time and received signal strength of the wireless signal with a fingerprinting model to address the multipath, non-line-of-sight, signal attenuation, and interference challenges of the indoor environments. Moreover, when leveraging the round trip time measurements, WiNar does not require clock synchronization between the transmitter and the receiver. We discuss the different components of the system and its implementation on the Android operating system. Our results show that WiNar has a sub-meter localization accuracy with an average localization error of less than 0.77 meters for two different testbeds. This accuracy outperforms the performance of the traditional signal strength (RSS) fingerprinting technique by at least 50% and ranging-based multi-lateration technique by at least 193%. Finally, our system is also robust to heterogeneous devices.



中文翻译:

使用 IEEE 802.11mc 往返时间进行准确的室内定位

无线 LAN 协议最近支持 WiFi 飞行时间 (ToF) 测量,以改进 WiFi 定位。具体来说,IEEE 802.11-2016 标准有一个精细时间测量 (FTM) 协议,可用于测量 WiFi 信号往返时间 (RTT)。在本文中,我们介绍了 WiNar 的设计和实现,这是一种基于 WiFi RTT 的室内定位系统,结合了基于指纹和测距技术的优势,以克服室内环境的不同挑战。WiNar 使用现成的商用接入点和移动电话,利用指纹模型利用无线信号的传播时间和接收信号强度来解决多径、非视距、信号衰减和干扰问题室内环境的挑战。而且,当利用往返时间测量时,WiNar 不需要发射器和接收器之间的时钟同步。我们讨论系统的不同组件及其在 Android 操作系统上的实现。我们的结果表明,WiNar 具有亚米级定位精度,两个不同测试台的平均定位误差小于 0.77 米。这种精度比传统的信号强度 (RSS) 指纹技术至少高出 50%,比基于测距的多点定位技术至少高出 193%。最后,我们的系统对异构设备也很健壮。我们讨论系统的不同组件及其在 Android 操作系统上的实现。我们的结果表明,WiNar 具有亚米级定位精度,两个不同测试台的平均定位误差小于 0.77 米。这种精度比传统的信号强度 (RSS) 指纹识别技术至少高出 50%,比基于测距的多点定位技术至少高出 193%。最后,我们的系统对异构设备也很健壮。我们讨论系统的不同组件及其在 Android 操作系统上的实现。我们的结果表明,WiNar 具有亚米级定位精度,两个不同测试台的平均定位误差小于 0.77 米。这种精度比传统的信号强度 (RSS) 指纹技术至少高出 50%,比基于测距的多点定位技术至少高出 193%。最后,我们的系统对异构设备也很健壮。这种精度比传统的信号强度 (RSS) 指纹技术至少高出 50%,比基于测距的多点定位技术至少高出 193%。最后,我们的系统对异构设备也很健壮。这种精度比传统的信号强度 (RSS) 指纹技术至少高出 50%,比基于测距的多点定位技术至少高出 193%。最后,我们的系统对异构设备也很健壮。

更新日期:2021-06-11
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