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基于到达时间差（TDoA）的定位在LoRaWAN中的理论与实践性能研究。

Investigation of the Performance of TDoA-Based Localization Over LoRaWAN in Theory and Practice.

作者信息

Pospisil Jan, Fujdiak Radek, Mikhaylov Konstantin

机构信息

Department of Telecommunications, Brno University of Technology, Technicka 12, 616 00 Brno, Czech Republic.

Centre for Wireless Communications, University of Oulu, Erkki Koiso-Kanttilan katu 3, 90014 Oulu, Finland.

出版信息

Sensors (Basel). 2020 Sep 23;20(19):5464. doi: 10.3390/s20195464.

DOI:10.3390/s20195464
PMID:32977644
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7582685/
Abstract

The paper deals with the localization in a low-power wide-area-network (LPWAN) operating long-range wide-area-network (LoRaWAN) technology. The LoRaWAN is, today, one of the most widely used connectivity-enabling technologies for the battery-powered smart devices employed in a broad range of applications. Many of these applications either require or can benefit from the availability of geolocation information. The use of global positioning system (GPS) technology is restrained by the bad propagation of the signal when the device is hidden indoors, and by energy consumption such a receiver would require. Therefore, this paper focuses on an alternative solution implying the use of the information readily available in the LoRaWAN network and application of the time difference of arrival (TDoA) method for the passive geolocation of end-devices in the network. First, the limits of geolocation services in networks that use narrow-band communication channels are discussed, as well as the relevant challenges faced by the TDoA approach. Then, we select five classic TDoA algorithms and evaluate their performance using simulation. Based on these results, we select the two providing the best accuracy (i.e., Chan's and Foy's). These algorithms were tested by the field measurements, using the specially designed low-cost gateways and test devices to estimate their real-life performance.

摘要

本文探讨了在采用远距离广域网（LoRaWAN）技术的低功耗广域网（LPWAN）中的定位问题。如今，LoRaWAN是应用广泛的电池供电智能设备中使用最普遍的连接支持技术之一。这些应用中的许多要么需要地理定位信息，要么能从其可用性中受益。当设备隐藏在室内时，全球定位系统（GPS）技术的使用受到信号传播不佳的限制，以及这种接收器所需的能量消耗的限制。因此，本文重点关注一种替代解决方案，即利用LoRaWAN网络中现成的信息，并应用到达时间差（TDoA）方法对网络中的终端设备进行被动地理定位。首先，讨论了使用窄带通信信道的网络中地理定位服务的局限性，以及TDoA方法面临的相关挑战。然后，我们选择了五种经典的TDoA算法，并通过仿真评估它们的性能。基于这些结果，我们选择了精度最高的两种算法（即Chan算法和Foy算法）。使用专门设计的低成本网关和测试设备通过实地测量对这些算法进行测试，以评估它们在实际应用中的性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/8d707dbc551a/sensors-20-05464-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/e0c1eea0c0e9/sensors-20-05464-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/12fa38adcc33/sensors-20-05464-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/ce926f530cb6/sensors-20-05464-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/a1f7f5df466d/sensors-20-05464-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/c6f459b94aa0/sensors-20-05464-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/dae4c5aee52a/sensors-20-05464-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/e997243ce8e9/sensors-20-05464-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/6327aea2c127/sensors-20-05464-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/75f1a299c696/sensors-20-05464-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/62e734c29d39/sensors-20-05464-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/7fd220919f81/sensors-20-05464-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/1d743e5f1933/sensors-20-05464-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/edac88a7a062/sensors-20-05464-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/8d707dbc551a/sensors-20-05464-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/e0c1eea0c0e9/sensors-20-05464-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/12fa38adcc33/sensors-20-05464-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/ce926f530cb6/sensors-20-05464-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/a1f7f5df466d/sensors-20-05464-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/c6f459b94aa0/sensors-20-05464-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/dae4c5aee52a/sensors-20-05464-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/e997243ce8e9/sensors-20-05464-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/6327aea2c127/sensors-20-05464-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/75f1a299c696/sensors-20-05464-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/62e734c29d39/sensors-20-05464-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/7fd220919f81/sensors-20-05464-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/1d743e5f1933/sensors-20-05464-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/edac88a7a062/sensors-20-05464-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/7582685/8d707dbc551a/sensors-20-05464-g016.jpg

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