Two-Dimensional RSSI-Based Indoor Localization Using Multiple Leaky Coaxial Cables With a Probabilistic Neural Network

Junjie Zhu,Kenta Nagayama,Rian Ferdian,Pengcheng Hou,Satoshi Denno,Yafei Hou

doi:10.1109/access.2022.3153083

Junjie Zhu, Kenta Nagayama + Show 4 more

Open Access

https://doi.org/10.1109/access.2022.3153083

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Journal: IEEE Access	Publication Date: Jan 1, 2022
Citations: 10	License type: CC BY 4.0

Affiliation: Okayama University, Andalas University

Abstract

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.

Highlights

In the last couple of years, due to the large-scale commercialization of the fifth-generation (5G) mobile communication technology and the explosive growth of the number of smart devices, various services and applications have emerged to change people’s lives
As a performance investigation for the Received signal strength indicator (RSSI)-based indoor localization using leaky coaxial cables (LCX), we utilize a probabilistic neural network (PNN) to find locations using the RSSI data received by LCX in indoor environments
The results show the performance of the RSSI-PNN method is promising and is better than the conventional time difference of arrival (TDOA) method

Summary

INTRODUCTION

In the last couple of years, due to the large-scale commercialization of the fifth-generation (5G) mobile communication technology and the explosive growth of the number of smart devices, various services and applications have emerged to change people’s lives. As a performance investigation for the RSSI-based indoor localization using LCX, we utilize a probabilistic neural network (PNN) to find locations using the RSSI data received by LCX in indoor environments. Compared with the conventional TDOA method, the proposal improves the localization accuracy by machine learning RSSI data samples from LCXs. We provide numerical simulation experiments as a performance investigation for the proposal. Due to the slot structure design, the V-type LCX in Fig. 2(b) has bi-directional radiation property if we input signals to both ends of the cable simultaneously [21]. In the conventional localization method, V-type LCX uses the signal radiation angle and TDOA value to estimate the location of the target user.

CHANNEL MODELING FOR LCX

Findings

CONCLUSION