Machine Learning Based Approach for Indoor Localization Using Ultra-Wide Bandwidth (UWB) System for Industrial Internet of Things (IIoT)

Abstract

With the rapid development of wireless communication technology and the emergence of the Industrial Internet of Things (IIoT)s applications, high-precision Indoor Positioning Services (IPS) are urgently required. While the Global Positioning System (GPS) has been a key technology for outdoor localization, its limitation for indoor environments is well known. Ultra-WideBand (UWB) can help provide a very accurate position or localization for indoor harsh propagation environments. This paper focuses on improving the accuracy of the UWB indoor localization system including the Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) conditions by developing a Machine Learning (ML) algorithm. In this paper, a Naive Bayes (NB) ML algorithm is developed for UWB IPS. The performance of the developed algorithm is evaluated by Receiving Operating Curves (ROC)s. The results indicate that by employing the NB based ML algorithm significantly improves the localization accuracy of the UWB system for both the LoS and NLoS environment.

A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges

“…The estimates obtained were accurate and 95% of the computed positions were shown to have a positioning distance error of less than 27.1 cm [155]. In [156], machine learning was employed to improve the RMSE of UWB based ranging estimates, and the positioning distance error was reduced to less than 10 cm. The UWB transmitter employs low power signals which are below −41.3 dBm/MHz emission limit.…”

Device Free Detection in Impulse Radio Ultrawide Bandwidth Systems

“…However, different modulation schemes such as on–off keying (OOK) [ 34 ], pulse amplitude modulation (PAM) [ 34 ] and pulse shape modulation (PSM) [ 35 , 36 ], and different multiple access schemes such as direct-sequence (DS) [ 37 ] and hybrid Direct-Sequence and Time-Hopping (DS-TH) [ 38 ] have been implemented for UWB systems. In contrast to the received signal strength based positioning, UWB exploits either the time of arrival (ToA) or time difference of arrival (TDoA) for real-time positioning [ 39 , 40 ]. In [ 41 ], authors proposed an indoor positioning system based on UWB and long-range (LoRa) wireless technologies.…”

“…An elderly people tracking system based on the combination of UWB and Bluetooth low energy has been proposed in [ 45 ]. Artificial intelligence and machine learning techniques were developed in [ 39 , 40 ] to improve the indoor localisation to less than 10 cm by classifying them into line of sight (LoS) and non-LoS environments. All of these approaches used experimental demonstrations and no analytical framework was developed for a UWB system.…”

“…This new method can be easily extended to existing device-free and device-based techniques and is easily applicable to any pulse-based UWB system using arbitrary multiple access or a modulation scheme. The main contributions of this paper are summarised as follows: The proposed detection method utilizes time domain concepts, which are different from the frequency-based approaches discussed in [ 39 , 40 , 42 , 43 , 46 ]. In the context of using an UWB system as an indoor radar, the Neyman–Pearson (NP) criteria is applied.…”

“…The proposed detection method utilizes time domain concepts, which are different from the frequency-based approaches discussed in [ 39 , 40 , 42 , 43 , 46 ].…”