Difference between revisions of "Ultra-wideband Concurrent Ranging"
From iis-projects
(→Character) |
(→Introduction) |
||
Line 20: | Line 20: | ||
= Introduction = | = Introduction = | ||
+ | [[File:uwb_scenario.png|thumb|right|320px| test]] | ||
Ultra-wideband (UWB) is one of the most promising and adopted ranging (i.e., distance measuring) technologies used for positioning and localization, as it enables centimeter-precision distance estimation and data transmission. In our applications, we use UWB with the time-of-arrival (ToA) technique, which determines the distance between two UWB nodes based on the travel time of a radio signal from the transmitter to the receiver. Due to its high-precision ranging, UWB enables range-based localization. | Ultra-wideband (UWB) is one of the most promising and adopted ranging (i.e., distance measuring) technologies used for positioning and localization, as it enables centimeter-precision distance estimation and data transmission. In our applications, we use UWB with the time-of-arrival (ToA) technique, which determines the distance between two UWB nodes based on the travel time of a radio signal from the transmitter to the receiver. Due to its high-precision ranging, UWB enables range-based localization. | ||
Revision as of 22:58, 18 January 2022
Contents
Overview
Status: Available
- Type: Master Thesis, Semester Project
- Professor: Prof. Dr. L. Benini
- Supervisors:
Introduction
Ultra-wideband (UWB) is one of the most promising and adopted ranging (i.e., distance measuring) technologies used for positioning and localization, as it enables centimeter-precision distance estimation and data transmission. In our applications, we use UWB with the time-of-arrival (ToA) technique, which determines the distance between two UWB nodes based on the travel time of a radio signal from the transmitter to the receiver. Due to its high-precision ranging, UWB enables range-based localization.
Project
Character
- 20% Literature / familiarization with UWB
- 30% Bare-metal / FreeRTOS C programming
- 30% Signal processing / machine learning
- 20% Evaluation
Prerequisites
- Strong interest in computer architecture
- Experience with digital design in SystemVerilog as taught in VLSI I
- Experience with low-level programming