Two-Dimensional RSSI-Based Indoor Localization Using Multiple Leaky Coaxial Cables With a Probabilistic Neural Network
Two-Dimensional RSSI-Based Indoor Localization Using Multiple Leaky Coaxial Cables With a Probabilistic Neural Network
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DOI：
10.1109/access.2022.3153083
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发表时间：
2022
期刊：
影响因子：
3.9
通讯作者：
Junjie Zhu;Pengcheng Hou;Kenta Nagayama;Yafei Hou;S. Denno;Rian Ferdian
中科院分区：
文献类型：
--
作者：
Junjie Zhu;Pengcheng Hou;Kenta Nagayama;Yafei Hou;S. Denno;Rian Ferdian
基于接收信号强度指示器(RSSI)的室内定位技术在许多位置感知应用中具有不可替代的优势。越来越明显的是，在第五代(5G)和未来通信技术的发展中，室内定位技术将在智能家居系统、制造自动化、医疗保健和机器人等基于位置的应用场景中发挥关键作用。与传统单极子天线的无线覆盖相比，漏泄同轴电缆可以在铁路车站、地下商场等狭长的线性小区或不规则环境中产生均匀稳定的无线覆盖，特别是对于一些制造工厂的无线区域来自大量心理机器的工厂。本文提出了一种在室内多径丰富环境中使用多条漏泄同轴电缆(LCX)的定位方法。与传统的基于到达时间(TOA)或到达时间差(TDOA)的定位方法不同，我们考虑通过机器学习来自LCX的RSSI来提高定位精度。我们将利用来自LCX的RSSI提出一种概率神经网络(PNN)方法。该方案针对的是弹道中的二维定位。此外，我们还比较了基于RSSI的PNN(RSSI-PNN)方法和传统TDOA方法在相同环境下的性能。结果表明，RSSI-PNN方法具有很好的应用前景，90%以上的定位误差在1m以内，与传统的TDOA方法相比，RSSI-PNN方法具有更好的定位性能，特别是在室内环境下无线覆盖的中间区域。
Received signal strength indicator (RSSI) based indoor localization technology has its irreplaceable advantages for many location-aware applications. It is becoming obvious that in the development of fifth-generation (5G) and future communication technology, indoor localization technology will play a key role in location-based application scenarios including smart home systems, manufacturing automation, health care, and robotics. Compared with wireless coverage using conventional monopole antenna, leaky coaxial cables (LCX) can generate a uniform and stable wireless coverage over a long-narrow linear-cell or irregular environment such as railway station and underground shopping-mall, especially for some manufacturing factories with wireless zone areas from a large number of mental machines. This paper presents a localization method using multiple leaky coaxial cables (LCX) for an indoor multipath-rich environment. Different from conventional localization methods based on time of arrival (TOA) or time difference of arrival (TDOA), we consider improving the localization accuracy by machine learning RSSI from LCX. We will present a probabilistic neural network (PNN) approach by utilizing RSSI from LCX. The proposal is aimed at the two-dimensional (2-D) localization in a trajectory. In addition, we also compared the performance of the RSSI-based PNN (RSSI-PNN) method and conventional TDOA method over the same environment. The results show the RSSI-PNN method is promising and more than 90% of the localization errors in the RSSI-PNN method are within 1 m. Compared with the conventional TDOA method, the RSSI-PNN method has better localization performance especially in the middle area of the wireless coverage of LCXs in the indoor environment.