Authors:
Afaz Uddin Ahmed
1
;
Reza Arablouei
2
;
Frank de Hoog
2
;
Branislav Kusy
2
and
Raja Jurdak
1
Affiliations:
1
School of Information Technology and Electrical Engineering, University of Queensland, St. Lucia, QLD 4072, Australia, Data61 Group, CSIRO, QLD 4069 and Australia
;
2
School of Information Technology and Electrical Engineering, University of Queensland, St. Lucia, QLD 4072 and Australia
Keyword(s):
Data Fusion, Multi-radio, Indoor Localization, WiFi, Channel State Information, Bluetooth Low Energy.
Related
Ontology
Subjects/Areas/Topics:
Aggregation, Classification and Tracking
;
Data Manipulation
;
Localization and Positioning Schemes
;
Modeling, Algorithms, and Performance Evaluation
;
Sensor Networks
;
Sensor, Mesh and Ad Hoc Communications and Networks
;
Signal Processing
;
Statistical and Adaptive Signal Processing
;
Telecommunications
;
Wireless Information Networks and Systems
Abstract:
Location estimation through fusing the information obtainable from multiple radio systems can reduce the dependency on each system and improves the performance. Research on fusion-based indoor localization using WiFi and Bluetooth-low-energy (BLE) beacons has mostly been limited to training-based approaches. In this paper, we propose a training-free indoor localization technique using received signals from WiFi and BLE device. The proposed technique estimates the position of the user device by fusing the information that it gains regarding the position of the target from the WiFi channel state information (CSI) and the RSSI measurements of BLE beacons. We use the WiFi CSI to estimate the angle of arrival (AoA), which we then use in conjunction with the RSSI measurements from the BLE beacons to develop a multi-radio fusion framework for indoor localization. We use a weighted centroid localization method to obtain an initial position estimate from the RSSI measurements. The initial pos
ition estimation helps to resolve the ambiguities in the AoA. The proposed technique is based on maximum-likelihood estimation (MLE) exploiting the probability density functions of the estimated AoA and the RSSI-induced distances. Simulation results show that the proposed technique improves the localization accuracy by 30% in a typical indoor environment compared with previous approaches.
(More)