Skip to main navigation
Skip to search
Skip to main content
Korea University Pure Home
Search content at Korea University Pure
Home
Profiles
Research units
Equipment
Research output
Press/Media
Prizes
Indoor pedestrian localization using ibeacon and improved kalman filter
Kwangjae Sung
, Dong Kyu Roy Lee
,
Hwangnam Kim
*
*
Corresponding author for this work
Research output
:
Contribution to journal
›
Article
›
peer-review
36
Link opens in a new tab
Citations (Scopus)
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Indoor pedestrian localization using ibeacon and improved kalman filter'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Bayesian Filter
16%
Bayesian Filtering
16%
Big Challenges
16%
Computational Cost
33%
Computational Efficiency
16%
Computational Energy
33%
Cost Efficiency
16%
Dead Reckoning
50%
Drift Error
16%
Energy Efficiency
33%
Energy Localization
16%
Error Bias
16%
Filter Method
16%
High Computational Efficiency
16%
High Performance
16%
High-precision Positioning
33%
Human Being
16%
IBeacon
100%
Improved Kalman Filter
100%
Indoor Environment
16%
Indoor Localization
16%
Indoor Pedestrian Localization
100%
Indoor Pedestrian Positioning
16%
Indoor Positioning
16%
Indoor Positioning System
16%
Integration Approach
16%
Kalman Filter
50%
Kalman Particle Filter
83%
Large Bias
16%
Learning Scheme
16%
Localization Accuracy
16%
Localization Performance
16%
Localization Scheme
33%
Location Information
16%
Location-aware
33%
Low-cost Inertial Sensor
16%
Machine Learning
16%
Particle Filter
100%
Particle Filtering
50%
Particle Method
16%
Particle number
16%
Pedestrian Positioning
16%
Popular
16%
Position Determination
16%
Positional Information
16%
Positioning Accuracy
33%
Positioning Approach
16%
Positioning System
33%
Radio Signal
16%
Random Motion
16%
Real Environment
16%
RSSI Fingerprinting
50%
Satisfactory Performance
16%
Sensor Motion
16%
Sigma Points
83%
Smartphone
16%
Unscented Kalman Filter
50%
Unscented Transformation
16%
User Motion
16%
Weighting Method
16%
Engineering
Bayesian Filtering
9%
Computational Cost
18%
Computational Efficiency
18%
Conservation of Energy
18%
Cost Efficiency
18%
Filtering Algorithm
27%
Frequency Signal
9%
Indoor Positioning Systems
9%
Inertial Sensor
9%
Kalman Filter
100%
Learning Scheme
9%
Learning System
9%
Localization Accuracy
9%
Localization Performance
9%
Particle Filter
100%
Radio Frequency
9%
Random Motion
9%
Sigma Point
45%
Smartphone
9%
Computer Science
Computational Cost
18%
Computational Efficiency
18%
Dead Reckoning
27%
Energy Efficiency
18%
Energy Efficient
9%
Filtering Method
9%
Kalman Filter
100%
Location Data
9%
Machine Learning Scheme
9%
Particle Filter
100%
Radio Frequency Signal
9%
Smartphone
9%