Phase difference of arrival range estimation for OFDM signal

Location information has become essential in information technology. Due to the blocking of buildings and other factors, the mature and stable Global Positioning System (GPS) is not applicable in the indoor environment. So, accurate indoor locating has become an essential technology. Phase Differenc...

Full description

Saved in:
Bibliographic Details
Main Author: Wang, Yiyi
Other Authors: Tay Wee Peng
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/164859
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
Description
Summary:Location information has become essential in information technology. Due to the blocking of buildings and other factors, the mature and stable Global Positioning System (GPS) is not applicable in the indoor environment. So, accurate indoor locating has become an essential technology. Phase Difference of Arrival (PDOA) ranging is one of the indoor positioning technologies. Because the carrier phase is highly correlated with the signal’s time of flight (TOF), the distance calculation method based on the carrier phase can theoretically achieve higher accuracy. However, the complex indoor channel environment, including multipath and non-line-of-sight propagation problems, brings troubles to achieving high-precision indoor positioning. Orthogonal Frequency Division Multiplexing (OFDM) signal has good anti-multipath performance. We can obtain a large number of phase information in one estimation due to the multi-carrier characteristic of OFDM. Besides, with the popularization of 5G, it is easy to implement OFDM signal-based PDOA ranging with the support of existing equipment. In this dissertation, we improve some proposed multifrequency-based range estimations to be better implemented in the OFDM system. Then, we test the improved range estimations with different wireless channel models, including the Additive White Gaussian noise (AWGN), Rayleigh, and Rician channels.