Indoor Positioning System (IPS) Using Ultra-Wide Bandwidth (UWB)--For Industrial Internet of Things (IIoT)  
[PERSON]  
1 Department of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3 DH, UK; [EMAIL_ADDRESS] (F.C. ); [EMAIL_ADDRESS] (P.L.L.) [PERSON]  
1 Department of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3 DH, UK; [EMAIL_ADDRESS] (F.C. ); [EMAIL_ADDRESS] (P.L.L.) [PERSON]  
1 Department of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3 DH, UK; [EMAIL_ADDRESS] (P.L.L.) [PERSON]  
2 College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand; [EMAIL_ADDRESS]  
[PERSON]  
3 Department of Computer Science, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8 NS, UK; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]; Tel: +44-(0)-148447397  
accuracy in larger-coverage areas in harsh environments with only a few reference anchors and an effective radiated power (ERP) of 2W [30; 31; 32]. UWB has several attractive properties, including high channel capacity because of its extremely wide bandwidth, which enables low transmission power, and the extremely short time duration of the pulses (typically nano- or picoseconds), which reduces multipath fading. The robustness to multipath effects and high temporal resolution also make UWB a suitable technology for high-precision localization by enabling precise ranging based on time of arrival (ToA), time difference of arrival (TDoA) and two-way time of arrival (TW-ToA) techniques [33; 34]. Several survey articles have been written on indoor localization, focusing on different technologies and their strengths and weaknesses. [PERSON] et al. [35] conducted a comprehensive survey of different types of indoor positioning techniques, considering factors, including the efficiency, hardware cost, reception, latency, scalability, and localization accuracy. They also highlight the challenges that need to be addressed to achieve accurate positioning systems. [PERSON] et al. [36] reviewed the use of data mining technology in an indoor positioning system for IoT applications and how data mining can help overcome the challenges. [PERSON] et al. [37] conducted a detailed analysis, including the strengths, weaknesses, opportunities, and threats, of UWB positioning technologies. Additionally, [PERSON] et al. [38] explored the potential applications of IPSs in the industrial sector. However, these articles do not provide a detailed discussion on the effects of the non-line-of-sight (NLoS) signal and some existing cutting-edge machine learning (ML) algorithms that can be used to classify NLoS signals. There is a need for a comprehensive review that summarizes these wireless technologies for IPSs and ML algorithms for UWB to provide a better understanding of future research directions. Firstly, this work presents an up-to-date applications of IPSs and the wireless technologies that can be used for IPS. Then, UWB characteristics and principles of position estimation methods and algorithms are discussed. The second objective of this article is to present a review of the NLoS signal's effects on the UWB positioning system. Finally, the article discusses the existing ML algorithms used to classify or mitigate the positioning error caused by NLoS signals and the main challenges for further work. The key contributions of this work are as follows:  
1. This work provides a detailed survey of the most common wireless communication-based technologies for IPSs and evaluates these technologies using an evaluation framework to highlight their pros and cons. 2. This paper provides a detailed discussion of various principles of position estimation methods that can be used for IPS, and highlights the advantages and limitations of using algorithms for UWB IPSs. 3. In addition, this paper presents a detailed explanation of UWB. Then, it presents an overview of the unique characteristics of UWB technology and the challenges still faced by IPS implementation. 4.