Position estimation for indoor navigation
2024 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE credits
Student thesis
Abstract [en]
This project investigates developing and implementing innovative indoornavigation systems by leveraging repurposed Wi-Fi infrastructure anddedicated RFM69HCW transceivers. Aimed at enhancing indoor positioningaccuracy, the study explores the viability of using Received Signal StrengthIndicator (RSSI) and dedicated device localization techniques to overcomethe limitations of existing Global Positioning System (GPS) technology inindoor environments. Through the design and testing of a printed circuit board(PCB) prototype that connects Raspberry Pi Pico (RPP) to RFM69HCWmodules and the development of custom drivers for the RP2040 processor,this research addresses the challenges of indoor navigation, such as signalvariability and environmental interference. The project also emphasizes theimportance of sustainable technology development by repurposing electronicwaste for innovative applications. Findings from the study reveal the potentialof these methodologies to improve indoor positioning accuracy despitechallenges related to hardware compatibility and the dynamic nature of indoorspaces. This research contributes to indoor navigation by demonstrating thefeasibility of using repurposed and dedicated hardware solutions, offeringinsights into future directions for enhancing indoor navigation systems, andhighlighting the role of sustainability in technological innovation.
Place, publisher, year, edition, pages
2024. , p. 56
Keywords [en]
RRSI (Received Signal Strength Indication), Trilateration, RFM69HCW (a specific RF transceiver module), 2D regression analysis, PCB Design (Printed Circuit Board Design), Coplanar Waveguide transmission line
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hh:diva-53233OAI: oai:DiVA.org:hh-53233DiVA, id: diva2:1852863
Subject / course
Electronics
Educational program
Intelligent Systems, 300 credits
Supervisors
Examiners
2024-03-222024-04-192025-10-01Bibliographically approved