An Ultra-wideband Battery-less Positioning System for Space Applications

An ultra-wide bandwidth (UWB) remote-powered positioning system for potential use in tracking floating objects inside space stations is presented. It makes use of battery-less tags that are powered-up and addressed through wireless power transfer in the UHF band and embed an energy efficient pulse generator in the 3-5 GHz UWB band. The system has been mounted on the ESA Mars Rover prototype to demonstrate its functionality and performance. Experimental results show the feasibility of centimeter-level localization accuracy at distances larger than 10 meters, with the capability of determining the position of multiple tags using a 2W-ERP power source in the UHF RFID frequency band.Comment: Published in: 2019 IEEE International Conference on RFID Technology and Applications (RFID-TA

Effiziente Lokalisierung von Nutzern und Geräten in Smarten Umgebungen

The thesis considers determination of location of sensors and users in smart environments using measurements of Received Signal Strength (RSS). The first part of the thesis focuses on localization in Wireless Sensor Networks and contributes two fully distributed algorithms which address the Sensor Selection Problem and provide the best trade-off between energy consumption and localization accuracy among the algorithms considered. Furthermore, the thesis contributes to Device Free Localization an indoor localization concept providing scalable and highly accurate location estimates (prototype: 0.36m² MSE) while using a COTS passive RFID-System and not relying on user-carried sensors

HF RFID tag location using magneto-inductive waves

Location of passive RFID tags in the HF regime presents significant problems, because of the absence of radiating fields at the low frequencies involved. Here we present a solution for one-dimensional localization based on magneto-inductive (MI) waves. Passive tags are interrogated using a travelling wave antenna based on a MI waveguide, a magnetically coupled array of L−C resonators supporting travelling waves. Load modulation signals generated by the tag during its unique identifier response are coupled into the waveguide and travel to either end with low group velocity. Signal timings are measured by cross-correlation, and the tag position is estimated to the nearest resonant loop from the difference in their arrival times. Correlation detection is demonstrated using a system model, and theoretical predictions are confirmed using an experimental system containing eleven transformer-coupled resonators operating at 13.56 MHz frequency. Accurate localization is obtained up to the tag reading limit using <1W RF power

Real-time localization using received signal strength

Locating and tracking assets in an indoor environment is a fundamental requirement for several applications which include for instance network enabled manufacturing. However, translating time of flight-based GPS technique for indoor solutions has proven very costly and inaccurate primarily due to the need for high resolution clocks and the non-availability of reliable line of sight condition between the transmitter and receiver. In this dissertation, localization and tracking of wireless devices using radio signal strength (RSS) measurements in an indoor environment is undertaken. This dissertation is presented in the form of five papers. The first two papers deal with localization and placement of receivers using a range-based method where the Friis transmission equation is used to relate the variation of the power with radial distance separation between the transmitter and receiver. The third paper introduces the cross correlation based localization methodology. Additionally, this paper also presents localization of passive RFID tags operating at 13.56MHz frequency or less by measuring the cross-correlation in multipath noise from the backscattered signals. The fourth paper extends the cross-correlation based localization algorithm to wireless devices operating at 2.4GHz by exploiting shadow fading cross-correlation. The final paper explores the placement of receivers in the target environment to ensure certain level of localization accuracy under cross-correlation based method. The effectiveness of our localization methodology is demonstrated experimentally by using IEEE 802.15.4 radios operating in fading noise rich environment such as an indoor mall and in a laboratory facility of Missouri University of Science and Technology. Analytical performance guarantees are also included for these methods in the dissertation --Abstract, page iv

Sensors and Systems for Indoor Positioning

This reprint is a reprint of the articles that appeared in Sensors' (MDPI) Special Issue on “Sensors and Systems for Indoor Positioning". The published original contributions focused on systems and technologies to enable indoor applications

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

TriSense: RFID, radar, and USRP-based hybrid sensing system for enhanced sensing and monitoring

This thesis presents a comprehensive approach to contactless human activity recognition (HAR) using the capabilities of three distinct technologies: radio frequency identification (RFID), Radar, and universal software-defined radio peripheral (USRP) for capturing and processing Wi-Fi-based signals. These technologies are then fused to enhance smart healthcare systems. The study initially utilises USRP devices to analyse Wi-Fi channel state information (CSI), choosing this over received signal strength for more accurate activity recognition. It employs a combination of machine learning and a hybrid of deep learning algorithms, such as the super learner and LSTM-CNN, for precise activity localisation. Subsequently, the study progresses to incorporate a transparent RFID tag wall (TRT-Wall) that employs a passive ultra-high frequency (UHF) RFID tag array. This RFID system has proven highly accurate in distinguishing between various activities, including sitting, standing, leaning, falling, and walking in two directions. Its effectiveness and non-intrusiveness make it particularly suited for elderly care, achieved using a modified version of the Transformer model without the use of a decoder. Furthermore, a significant advancement within this study is the creation of a novel fusion (RFiDARFusion) system, which combines RFID and Radar technologies. This system employs a long short-term memory networks variational autoencoder (LSTM-VAE) fusion model, utilising RFID amplitude and Radar RSSI data. This fusion approach significantly improves accuracy in challenging scenarios, such as those involving long-range and non-line-of-sight conditions. The RFiDARFusion system notably improves the detection of complex activities, highlighting its potential to reduce healthcare costs and enhance the quality of life for elderly patients in assisted living facilities. Overall, this thesis highlights the significant potential of radio frequency technologies with artif icial intelligence, along with their combined application, to develop robust, privacy-conscious, and cost-effective solutions for healthcare and assisted living monitoring systems