Indoor pedestrian localization using ibeacon and improved kalman filter

Kwangjae Sung, Dong Kyu Roy Lee, Hwangnam Kim

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

The reliable and accurate indoor pedestrian positioning is one of the biggest challenges for location-based systems and applications. Most pedestrian positioning systems have drift error and large bias due to low-cost inertial sensors and random motions of human being, as well as unpredictable and time-varying radio-frequency (RF) signals used for position determination. To solve this problem, many indoor positioning approaches that integrate the user’s motion estimated by dead reckoning (DR) method and the location data obtained by RSS fingerprinting through Bayesian filter, such as the Kalman filter (KF), unscented Kalman filter (UKF), and particle filter (PF), have recently been proposed to achieve higher positioning accuracy in indoor environments. Among Bayesian filtering methods, PF is the most popular integrating approach and can provide the best localization performance. However, since PF uses a large number of particles for the high performance, it can lead to considerable computational cost. This paper presents an indoor positioning system implemented on a smartphone, which uses simple dead reckoning (DR), RSS fingerprinting using iBeacon and machine learning scheme, and improved KF. The core of the system is the enhanced KF called a sigma-point Kalman particle filter (SKPF), which localize the user leveraging both the unscented transform of UKF and the weighting method of PF. The SKPF algorithm proposed in this study is used to provide the enhanced positioning accuracy by fusing positional data obtained from both DR and fingerprinting with uncertainty. The SKPF algorithm can achieve better positioning accuracy than KF and UKF and comparable performance compared to PF, and it can provide higher computational efficiency compared with PF. iBeacon in our positioning system is used for energy-efficient localization and RSS fingerprinting. We aim to design the localization scheme that can realize the high positioning accuracy, computational efficiency, and energy efficiency through the SKPF and iBeacon indoors. Empirical experiments in real environments show that the use of the SKPF algorithm and iBeacon in our indoor localization scheme can achieve very satisfactory performance in terms of localization accuracy, computational cost, and energy efficiency.

Original languageEnglish
Article number1722
JournalSensors (Switzerland)
Volume18
Issue number6
DOIs
Publication statusPublished - 2018 Jun 1

Fingerprint

Kalman filters
positioning
filters
Costs and Cost Analysis
RSS
Bayes Theorem
dead reckoning
Radio
Uncertainty
Computational efficiency
Energy efficiency
Costs
Smartphones
Pedestrians
Learning systems
costs
machine learning
Sensors
learning
energy

Keywords

  • Bluetooth beacon
  • Bluetooth low energy
  • Dead reckoning
  • Indoor positioning
  • Kalman filtering
  • Particle filtering
  • Received signal strength (RSS) fingerprinting
  • Sensor fusion

ASJC Scopus subject areas

  • Analytical Chemistry
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

Cite this

Indoor pedestrian localization using ibeacon and improved kalman filter. / Sung, Kwangjae; Lee, Dong Kyu Roy; Kim, Hwangnam.

In: Sensors (Switzerland), Vol. 18, No. 6, 1722, 01.06.2018.

Research output: Contribution to journalArticle

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