A Robust and Accurate Indoor Localization Using Learning-Based Fusion of Wi-Fi RTT and RSSI

Great attention has been paid to indoor localization due to its wide range of associated applications and services. Fingerprinting and time-based localization techniques are among the most popular approaches in the field due to their promising performance. However, fingerprinting techniques usually...

Full description

Saved in:
Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-03, Vol.22 (7), p.2700
Main Authors: Rizk, Hamada, Elmogy, Ahmed, Yamaguchi, Hirozumi
Format: Article
Language:English
Subjects:
Accuracy
canonical correlation analysis
Cellular telephones
Correlation analysis
Deep learning
Fingerprinting
Indoor environments
indoor localization
Localization
Noise
Performance evaluation
Propagation
Radio frequency identification
round-trip time
Signal strength
Time measurement
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Great attention has been paid to indoor localization due to its wide range of associated applications and services. Fingerprinting and time-based localization techniques are among the most popular approaches in the field due to their promising performance. However, fingerprinting techniques usually suffer from signal fluctuations and interference, which yields unstable localization performance. On the other hand, the accuracy of time-based techniques is highly affected by multipath propagation errors and non-line-of-sight transmissions. To combat these challenges, this paper presents a hybrid deep-learning-based indoor localization system called which fuses fingerprinting and time-based techniques with a view of combining their advantages. leverages a novel approach for fusing received signal strength indication (RSSI) and round-trip time (RTT) measurements and extracting high-level features using deep canonical correlation analysis. The extracted features are then used in training a localization model for facilitating the location estimation process. Different modules are incorporated to improve the deep model's generalization against overtraining and noise. The experimental results obtained at two different indoor environments show that improves localization accuracy by at least 267% and 496% compared to the state-of-the-art fingerprinting and ranging-based-multilateration techniques, respectively.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22072700