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Indoor Pedestrian Localization Using iBeacon and Improved Kalman Filter.

Kwangjae Sung1, Dong Kyu 'Roy' Lee2, Hwangnam Kim3

  • 1School of Electrical Engineering, Korea University, Seoul 02841, Korea. kjsung80@korea.ac.kr.

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Summary
This summary is machine-generated.

This study introduces a new Sigma-Point Kalman Particle Filter (SKPF) for accurate indoor pedestrian positioning. The SKPF system enhances accuracy and computational efficiency using dead reckoning and iBeacon RSS fingerprinting.

Keywords:
Bluetooth Low EnergyBluetooth beaconKalman filteringdead reckoningindoor positioningparticle filteringreceived signal strength (RSS) fingerprintingsensor fusion

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Area of Science:

  • Location-based services
  • Indoor positioning systems
  • Signal processing

Background:

  • Indoor pedestrian positioning faces challenges like drift error and signal variability.
  • Existing methods integrate dead reckoning (DR) with RSS fingerprinting using Bayesian filters (KF, UKF, PF).
  • Particle filters (PF) offer high accuracy but incur significant computational costs.

Purpose of the Study:

  • To develop an accurate, computationally efficient, and energy-efficient indoor positioning system for smartphones.
  • To introduce an enhanced Kalman filter, the Sigma-Point Kalman Particle Filter (SKPF), for improved localization.
  • To leverage iBeacon technology for energy-efficient RSS fingerprinting.

Main Methods:

  • Implementation of a smartphone-based indoor positioning system.
  • Integration of simple dead reckoning (DR) with iBeacon-based RSS fingerprinting.
  • Development and application of the Sigma-Point Kalman Particle Filter (SKPF) algorithm, combining UKF's unscented transform and PF's weighting method.

Main Results:

  • The SKPF algorithm demonstrated superior positioning accuracy compared to KF and UKF.
  • SKPF achieved performance comparable to PF but with significantly higher computational efficiency.
  • The system utilizing SKPF and iBeacon showed excellent performance in localization accuracy, computational cost, and energy efficiency.

Conclusions:

  • The proposed SKPF algorithm effectively fuses DR and fingerprinting data for enhanced indoor positioning accuracy.
  • The SKPF offers a practical solution balancing high accuracy with computational and energy efficiency.
  • The integration of SKPF and iBeacon provides a robust and efficient indoor localization scheme.