Indoor localization & tracking using ultra-wide band technology

This report analyzes the use of use of Ultra-Wideband (UWB) technology for the purpose of indoor localization via the use of Decawave’s DWM1001-DEV supported by the Raspberry Pi 4. The report explores the feasbility of a flexible and dynamic indoor localization system instead of a fixed Indoor Posit...

Full description

Saved in:
Bibliographic Details
Main Author: Choy, Kish Wai Kit
Other Authors: A S Madhukumar
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/175296
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
Description
Summary:This report analyzes the use of use of Ultra-Wideband (UWB) technology for the purpose of indoor localization via the use of Decawave’s DWM1001-DEV supported by the Raspberry Pi 4. The report explores the feasbility of a flexible and dynamic indoor localization system instead of a fixed Indoor Positioning System (IPS). Since the traditional Global Navigation Satellite System (GNSS) such as Global Position System (GPS) experiences significant interference from building structures, indoor solutions for localization techniques are henceforth required. With the rise of various technologies supporting indoor localization, a vast number of approaches to IPS is readily available. While each technology has their own advantages and disadvantages, UWB is the most promising of the technologies available in terms of its high accuracy with low power consumption as compared to competitors such as Wi-Fi, Bluetooth Low Energy (BLE) and Radio Frequency Identification (RFID). The proposed system in this experiment utilises time of flight (ToF) for distance measurements and trilateration techniques to identify the location of device node. Unlike other systems, the system proposed utilises the anchors as tags as well, thus allowing greater flexibility experiment allowing the device nodes to be dynamically placed serving as multi-role nodes. A Kalman Filter (KF) is also implemented to reduce the noise of the sensor data received, allowing for more accurate positioning. The device nodes all perform ranging between one another and stores a matrix of distances of all devices in the network, which is extracted via the serial port interface to a raspberry pi device serving the graphical user interface (GUI) collecting and displaying each devices’ location.