RSSI-Based Indoor Positioning via Adaptive Federated Kalman Filter

Loading...
Thumbnail Image

Date

2022

Authors

Kerestecioglu, Feza

Journal Title

Journal ISSN

Volume Title

Publisher

IEEE-Inst Electrical Electronics Engineers Inc

Open Access Color

OpenAIRE Downloads

OpenAIRE Views

Research Projects

Organizational Units

Journal Issue

Abstract

In this paper, federated Kalman filter (FKF) is applied for indoor positioning. Position information that is multi-laterated from the distance information obtained using the received signal strengths collected from several access points are processed in a FKF to estimate the position of the target. Two approaches are presented to adjust the information-sharing coefficients of FKF using online measurements. The data collected on a test bed composed of four access points are used to assess and compare the performances of the proposed algorithms. It is shown that the estimation error can be improved considerably by adjusting the information-sharing coefficients online.

Description

Keywords

Sensors, Kalman filters, Sensor fusion, Mathematical model, Sensor systems, Position measurement, Location awareness, Indoor positioning, Kalman filter, sensor fusion

Turkish CoHE Thesis Center URL

Fields of Science

Citation

4

WoS Q

Q2

Scopus Q

Q1

Source

Ieee Sensors Journal

Volume

22

Issue

6

Start Page

5302

End Page

5308