Calibration-Free Positioning Technique Using Wi-Fi Ranging and Built-in Sensors of Mobile Devices
March 12, 2020 Β· Declared Dead Β· π IEEE Internet of Things Journal
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Jeongsik Choi, Yang-Seok Choi
arXiv ID
2003.06013
Category
cs.NI: Networking & Internet
Cross-listed
eess.SP
Citations
48
Venue
IEEE Internet of Things Journal
Last Checked
6 months ago
Abstract
As positioning solutions integrate multiple components to improve accuracy, the number of parameters that require calibration has increased. This paper studies a calibration-free positioning technique using Wi-Fi ranging and pedestrian dead reckoning (PDR), where every parameter in the system is optimized in real-time. This significantly decreases the time and effort required to perform manual calibration procedures and enables the positioning solution to achieve robust performance in various situations. Additionally, this paper studies an efficient way of performing irregular Wi-Fi ranging procedures to improve battery life and network performance of mobile devices. The positioning performance of the proposed method was verified using a real-time Android application on several mobile devices under a large indoor office environment. Without any calibration, the proposed method achieved up to 1.38 m average positioning accuracy for received signal strength (RSS)-based ranging scenarios, which differs only by 30 cm from the benchmark assuming perfect calibration. In addition, the proposed method achieved up to 1.04 m accuracy for round trip time (RTT)-based ranging scenarios with a 40 MHz bandwidth configuration, which differs only by 10 cm from the benchmark.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Networking & Internet
R.I.P.
π»
Ghosted
π
π
The Cartographer
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
π
π
The Cartographer
A Survey of Indoor Localization Systems and Technologies
R.I.P.
π»
Ghosted
Survey of Important Issues in UAV Communication Networks
π
π
The Cartographer
Network Function Virtualization: State-of-the-art and Research Challenges
π
π
The Cartographer
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted