Accuracy Enhancement of RSSI-based Distance Estimation by Applying Gaussian Filter

Jea Gu Lee; Byung Kwan Kim; Sung Bong Jang; Seung Ho Yeon  and Young Woong Ko

doi:10.17485/ijst/2016/v9i20/94675

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Accuracy Enhancement of RSSI-based Distance Estimation by Applying Gaussian Filter

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2016/v9i20/94675

Year: 2016, Volume: 9, Issue: 20, Pages: 1-5

Original Article

Accuracy Enhancement of RSSI-based Distance Estimation by Applying Gaussian Filter

Jea-Gu Lee¹ , Byung-Kwan Kim¹ , Sung-Bong Jang² , Seung-Ho Yeon¹ and Young Woong Ko^1*

¹Department of Computer Engineering, College of Information & Electronic Engineering, Hallym University, Chuncheon, Gangwon, 200-702, Republic of Korea; [email protected], [email protected], [email protected], [email protected] ²Department of Computer Software Engineering, Kumoh National Institute of Technology, 61 Daehak-ro, Gumi, Kyoung-Buk - 730-701, Republic of Korea; [email protected]

*Author for correspondence
Young Woong Ko
Department of Computer Engineering, College of Information & Electronic Engineering, Hallym University, Chuncheon, Gangwon, 200-702, Republic of Korea;
Email: [email protected]

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

RSSI values of Bluetooth Low Energy (BLE) beacon are unreliable to use localization. To cope with this problem, we propose a new localization algorithm that enhances the accuracy of RSSI value. The proposed algorithm applies Gaussian filter to RSSI values from BLE Beacons, and then uses weight value based on filtered RSSI quality. Background/Objectives: Friis formula is used to calculate distance between a BLE beacon and BLE scanners using triangulation scheme. Finally, Gaussian filtering is applied twice to the location values for accuracy improvement. Findings: Experiments are performed in indoor environment, and experiment data is calculated by MATLAB to make graph and chart for easily comparing location result. Experiment result shows that DGF algorithm shows more accurate and reliable localization result than commonly used Kalman filter algorithm. Furthermore, DGF algorithm is very effective to calculate not only distance but also location. Improvements: DGF algorithm indicates excellent performance when we adapt weight value. The proposed system can be used to tracking the location of BLE beacon in real-time.

Keywords: Bluetooth, Beacon, DGF, Localization, RSSI