scholarly journals Corrigendum to “Method for Improving Indoor Positioning Accuracy Using Extended Kalman Filter”

2017 ◽  
Vol 2017 ◽  
pp. 1-1
Author(s):  
Seoung-Hyeon Lee ◽  
Il-Kwon Lim ◽  
Jae-Kwang Lee
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Indoor Positioning ◽  
Positioning Accuracy

scholarly journals Method for Improving Indoor Positioning Accuracy Using Extended Kalman Filter

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Seoung-Hyeon Lee ◽  
Il-Kwan Lim ◽  
Jae-Kwang Lee
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Low Cost ◽  
Indoor Positioning ◽  
Low Power Consumption ◽  
New Paradigm ◽  
Positioning Accuracy ◽  
Positioning Method ◽  
Beacon Signal ◽  
Dimensional Surface

Beacons using bluetooth low-energy (BLE) technology have emerged as a new paradigm of indoor positioning service (IPS) because of their advantages such as low power consumption, miniaturization, wide signal range, and low cost. However, the beacon performance is poor in terms of the indoor positioning accuracy because of noise, motion, and fading, all of which are characteristics of a bluetooth signal and depend on the installation location. Therefore, it is necessary to improve the accuracy of beacon-based indoor positioning technology by fusing it with existing indoor positioning technology, which uses Wi-Fi, ZigBee, and so forth. This study proposes a beacon-based indoor positioning method using an extended Kalman filter that recursively processes input data including noise. After defining the movement of a smartphone on a flat two-dimensional surface, it was assumed that the beacon signal is nonlinear. Then, the standard deviation and properties of the beacon signal were analyzed. According to the analysis results, an extended Kalman filter was designed and the accuracy of the smartphone’s indoor position was analyzed through simulations and tests. The proposed technique achieved good indoor positioning accuracy, with errors of 0.26 m and 0.28 m from the average x- and y-coordinates, respectively, based solely on the beacon signal.

scholarly journals Implementation of Hybrid Indoor Positioning System based on Wi-Fi and PDR in Smartphone

2019 ◽  
Vol 8 (2S6) ◽  
pp. 357-361
Keyword(s):  
Kalman Filter ◽  
Inertial Sensors ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Initial Position ◽  
Mobile User ◽  
Sensor Data ◽  
Positioning System ◽  
Fusion Algorithm ◽  
Positioning Accuracy

This paper proposed thehybridindoor positioning system in smartphone for positioning accuracy by fusion of wireless-fidelity (Wi-Fi) signals and inertial sensors from pedestrian dead reckoning (PDR) in smartphone. The proposed system uses Wi-Fi as the source of received signal strength indicator (RSSI) for fingerprint and smartphones sensor data from PDR. RSSI signals are used to determine the initial position and reduce error accumulation of PDR while smartphone sensor data are used to estimate user trajectory. Extended Kalman Filter (EKF) is the fusion algorithm used for its similarity with Kalman Filter (KF) but with advantages of processing non-linear progressions. An estimated 49 steps were detected which is identical to the 50 steps taken in the experiment while showing a trajectory similar to the actual route taken by the mobile user. A benefit of using built-in smartphone sensors is its cost-effectiveness and availability that does not require additional hardware. In addition, a nonlinear EKF is used to enhance the positioning accuracy in the proposed system. Further studies will be made in the potential of indoor positioning algorithm including the effect of noise interference on sensors and RSSI and the accumulated errors resulting from walking

scholarly journals Indoor Positioning Tightly Coupled Wi-Fi FTM Ranging and PDR Based on the Extended Kalman Filter for Smartphones

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 49671-49684 ◽  
Author(s):  
Meng Sun ◽  
Yunjia Wang ◽  
Shenglei Xu ◽  
Hongxia Qi ◽  
Xianxian Hu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Indoor Positioning ◽  
Tightly Coupled

Extended Kalman Filter for wireless LAN based indoor positioning

2008 ◽  
Vol 45 (4) ◽  
pp. 960-971 ◽  
Author(s):  
Jaegeol Yim ◽  
Chansik Park ◽  
Jaehun Joo ◽  
Seunghwan Jeong
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Wireless Lan ◽  
Indoor Positioning
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