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RFID: Hybrid Scene Analysis-Neural Network System for 3D Indoor Positioning optimal system arrangement approach
Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för tillämpad signalbehandling.
2014 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The purpose of this research is to find an optimal number and configuration of readers in RFID based 3D Indoor Positioning System. The system applies a Hybrid Scene Analysis - Neural Network algorithm to estimate target's position with a desired accuracy. The system's accuracy and cost depend on a number of utilized readers and their arrangement. Readers' deployment is crucial for the localization accuracy too. The system optimization enhances the system cost-efficiency. The arrangement analysis was based on simulations and validated by physical experiment. The results of this research define a trade-off between a number of readers and their deployment and the system performance in terms of localization accuracy.

sted, utgiver, år, opplag, sider
Montevideo: IEEE , 2014.
Emneord [en]
3D indoor positioning, neural network system, optimal system arrangement, RFID network planning, optimization, radiofrequency identification, reader configuration, scene analysis
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-6615DOI: 10.1109/I2MTC.2014.6860732Lokal ID: oai:bth.se:forskinfoB29987C9B8234376C1257D550029E77AOAI: oai:DiVA.org:bth-6615DiVA, id: diva2:834137
Konferanse
Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, 2014 IEEE International
Tilgjengelig fra: 2014-09-30 Laget: 2014-09-16 Sist oppdatert: 2019-03-28bibliografisk kontrollert
Inngår i avhandling
1. Real-time Locating Systems for indoor applications: the methodological customization approach
Åpne denne publikasjonen i ny fane eller vindu >>Real-time Locating Systems for indoor applications: the methodological customization approach
2019 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Emerging wireless technologies increase the potential and effectiveness of wireless Real-time Locating Systems (RTLSs), which precisely localize the position, and identify things and people in real time. Among many applications, RTLSs are widely used in the industrial sector for indoor logistics and safety applications. However, signal interferences, which affect the system’s performance, are a serious issue of all indoor RTLS applications. Among others, the interferences are caused by the changeable working environment, the geometry and structure of the space, furnishing, and other obstacles. A customization of the RTLS’s architecture and localization algorithm may provide a way to overcome the interference problem and then enhance the systems’ performance.

The objective of this thesis is to develop and implement customization methods, which enhance system performance in the changeable working environment without compromising the functional and non-functional requirements defined by future users and stakeholders. The customized solution is to be based on the comprehensive methodological analysis of the system’s technical and environmental constraints, along with the requirements specified by the application field. The customization process covers the selection, adjustment and adaptation of the wireless technologies and methods in order to enhance the location system’s performance, in terms of accuracy and precision without compromising its simplicity and price.

In this research, wireless technologies of Radio Frequency Identification (RFID) and Ultra-wideband (UWB) are applied. The related indoor localization methods, such as, ranging techniques based on Received Signal Strength (RSS) and Angle of Arrival (AoA), are a thesis focus. Moreover, estimation methods like Fingerprinting and Angulation are used.

One of the proposed customization methods of RFID-based 3D RTLS, refers to the heuristic analysis-based optimization of a number and configuration of readers. For the same type of system, an alternative way of performance improvement is a customization of localization algorithm, explicitly the Neural Network-based estimation algorithm and its structural features and training methods.

Also in this thesis, performance improvement methods of the AoA-based RTLS operating in an UWB technology are proposed. The proposed customization of this system type is based on the uncertainty pattern defined by a statistical uncertainty model, which maps the localization uncertainty in terms of precision in the 2D workspace. The model depicts how the localization uncertainty depends on an arrangement of Location Sensors and workspace geometry. Another proposed customization method is realized by defining and implementing correction vectors for different working environments, which enhance the system’s performance in terms of its accuracy.

This thesis consists of two parts. Part I, Prolegomena, presents the overview of applied theories and research methods. This part aims to illustrate the links between the articles constituting the second part of the dissertation. Part II, Papers consists of five reformatted papers already published in peer reviewed journals and conferences.

sted, utgiver, år, opplag, sider
Karlskrona: Blekinge Tekniska Högskola, 2019. s. 204
Serie
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 08
Emneord
Accuracy and Precision, Angle of Arrival, Fingerprinting Method, Indoor Localization, Indoor Positioning System, Multi-Sensor System, Neural Network, Radio Frequency Identification - RFID, Real Time Locating System, Received Signal Strength, RFID Network Planning, Scene Analysis, Sensors Arrangement, System Customization, Uncertainty, Uncertainty Map, User Driven Design
HSV kategori
Identifikatorer
urn:nbn:se:bth-17740 (URN)978-91-7295-373-4 (ISBN)
Disputas
2019-05-03, J1650, Blekinge Tekniska Högskola, Campus Gräsvik, Karlskrona, 13:15 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2019-03-28 Laget: 2019-03-25 Sist oppdatert: 2019-06-18bibliografisk kontrollert

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