Indoor wireless communications and applications

Chapter 3 addresses challenges in radio link and system design in indoor scenarios. Given the fact that most human activities take place in indoor environments, the need for supporting ubiquitous indoor data connectivity and location/tracking service becomes even more important than in the previous decades. Specific technical challenges addressed in this section are(i), modelling complex indoor radio channels for effective antenna deployment, (ii), potential of millimeter-wave (mm-wave) radios for supporting higher data rates, and (iii), feasible indoor localisation and tracking techniques, which are summarised in three dedicated sections of this chapter

Bidirectional UWB Localization: A Review on an Elastic Positioning Scheme for GNSS-deprived Zones

A bidirectional Ultra-Wideband (UWB) localization scheme is one of the three widely deployed design integration processes ordinarily destined for time-based UWB positioning systems. The key property of the bidirectional UWB localization is its ability to serve both the navigation and tracking assignments on-demand within a single localization scheme. Conventionally, the perspective of navigation and tracking in wireless localization systems is viewed distinctly as an individual system because different methodologies were required for the implementation process. The ability to flexibly or elastically combine two unique positioning perspectives (i.e., navigation and tracking) within a single scheme is a paradigm shift in the way location-based services are observed. Thus, this article addresses and pinpoints the potential of a bidirectional UWB localization scheme. Regarding this, the complete system model of the bidirectional UWB localization scheme was comprehensively described based on modular processes in this article. The demonstrative evaluation results based on two system integration processes as well as a SWOT (strengths, weaknesses, opportunities, and threats) analysis of the scheme were also discussed. Moreover, we argued that the presented bidirectional scheme can also be used as a prospective topology for the realization of precise location estimation processes in 5G/6G wireless mobile networks, as well as Wi-Fi fine-time measurement-based positioning systems in this article.Comment: 30 pages, 12 figure

Precise positioning of autonomous vehicles combining UWB ranging estimations with on-board sensors

In this paper, we analyze the performance of a positioning system based on the fusion of Ultra-Wideband (UWB) ranging estimates together with odometry and inertial data from the vehicle. For carrying out this data fusion, an Extended Kalman Filter (EKF) has been used. Furthermore, a post-processing algorithm has been designed to remove the Non Line-Of-Sight (NLOS) UWB ranging estimates to further improve the accuracy of the proposed solution. This solution has been tested using both a simulated environment and a real environment. This research work is in the scope of the PRoPART European Project. The different real tests have been performed on the AstaZero proving ground using a Radio Control car (RC car) developed by RISE (Research Institutes of Sweden) as testing platform. Thus, a real time positioning solution has been achieved complying with the accuracy requirements for the PRoPART use case

Time-based vs. Fingerprinting-based Positioning Using Artificial Neural Networks

High-accuracy positioning has gained significant interest for many use-cases across various domains such as industrial internet of things (IIoT), healthcare and entertainment. Radio frequency (RF) measurements are widely utilized for user localization. However, challenging radio conditions such as non-line-of-sight (NLOS) and multipath propagation can deteriorate the positioning accuracy. Machine learning (ML)-based estimators have been proposed to overcome these challenges. RF measurements can be utilized for positioning in multiple ways resulting in time-based, angle-based and fingerprinting-based methods. Different methods, however, impose different implementation requirements to the system, and may perform differently in terms of accuracy for a given setting. In this paper, we use artificial neural networks (ANNs) to realize time-of-arrival (ToA)-based and channel impulse response (CIR) fingerprinting-based positioning. We compare their performance for different indoor environments based on real-world ultra-wideband (UWB) measurements. We first show that using ML techniques helps to improve the estimation accuracy compared to conventional techniques for time-based positioning. When comparing time-based and fingerprinting schemes using ANNs, we show that the favorable method in terms of positioning accuracy is different for different environments, where the accuracy is affected not only by the radio propagation conditions but also the density and distribution of reference user locations used for fingerprinting.Comment: Accepted for presentation at International Conference on Indoor Positioning and Indoor Navigation (IPIN) 202

Analysis of GPS and UWB positioning system for athlete tracking

In recent years, wearable performance monitoring systems have become increasingly popular in competitive sports. Wearable devices can provide vital information including distance covered, velocity, change of direction, and acceleration, which can be used to improve athlete performance and prevent injuries. Tracking technology that monitors the movement of an athlete is an important element of sport wearable devices. For tracking, the cheapest option is to use global positioning system (GPS) data however, their large margins of error are a major concern in many sports. Consequently, indoor positioning systems (IPS) have become popular in sports in recent years where the ultra-wideband (UWB) positioning sensor is now being used for tracking. IPS promises much higher accuracy, but unlike GPS, it requires a longer set-up time and its costs are significantly more. In this research, we investigate the suitability of the UWB-based localisation technique for wearable sports performance monitoring systems. We implemented a hardware set-up for both positioning sensors, UWB and the GPS-based (both 10 Hz and 1 Hz) localisation systems, and then monitored their accuracy in 2D and 3D side-by-side for the sport of tennis. Our gathered data shows a major drawback in the UWB-based localisation system. To address this major drawback we introduce an artificial intelligent model, which shows some promising results

On Safety Enhancement in IIoT Scenarios through Heterogeneous Localization Techniques

In the field of the Industrial Internet of Things (IIoT), strictly related to Industry 4.0, one of the main aspects to be carefully considered by the governing board of a manufacturing company is the safety level to be guaranteed to the workers inside production plants. This involves daily human activities, together with production machines to be used during the working hour (and periodically maintained), and mobile industrial vehicles moving around the production plant. To this end, a precise localization of both workers and vehicles is expedient to improve the safety level—avoiding that people move inside forbidden areas or perform dangerous actions—as well as allowing a more accurate control and reporting to national authorities in charge of verifying the compliance to safety regulations (e.g., aggregated data, not shared outside the company, used to fill injuries reports in case of official inspections), in the presence of accidents and anomalous events. In this paper, we present the design of IIoT-related localization mechanisms exploiting heterogeneous communication technologies, in turn analysing how the localization can cope with the adoption of wideband (e.g., Wi-Fi) and narrowband (e.g., Narrowband IoT, NB-IoT) communication protocols and discussing how these communication paradigms may impact existing and modern production plants

PEOPLEx: PEdestrian Opportunistic Positioning LEveraging IMU, UWB, BLE and WiFi

This paper advances the field of pedestrian localization by introducing a unifying framework for opportunistic positioning based on nonlinear factor graph optimization. While many existing approaches assume constant availability of one or multiple sensing signals, our methodology employs IMU-based pedestrian inertial navigation as the backbone for sensor fusion, opportunistically integrating Ultra-Wideband (UWB), Bluetooth Low Energy (BLE), and WiFi signals when they are available in the environment. The proposed PEOPLEx framework is designed to incorporate sensing data as it becomes available, operating without any prior knowledge about the environment (e.g. anchor locations, radio frequency maps, etc.). Our contributions are twofold: 1) we introduce an opportunistic multi-sensor and real-time pedestrian positioning framework fusing the available sensor measurements; 2) we develop novel factors for adaptive scaling and coarse loop closures, significantly improving the precision of indoor positioning. Experimental validation confirms that our approach achieves accurate localization estimates in real indoor scenarios using commercial smartphones