Indoor positioning system using hybrid method of fingerprinting and pedestrian dead reckoning

被引：8

作者：

Riady, Alvin ^{[1
]}

Kusuma, Gede Putra ^{[1
]}

机构：

[1] Bina Nusantara Univ, BINUS Grad Program Master Comp Sci, Comp Sci Dept, Jakarta 11480, Indonesia

来源：

JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES | 2022年 / 34卷 / 09期

关键词：

Indoor positioning system; BLE fingerprinting method; Pedestrian dead reckoning; Artificial neural network; Support vector regression; FUSION;

D O I：

10.1016/j.jksuci.2021.09.005

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Indoor Positioning System has been one of the most attractive research after Bluetooth Low Energy (BLE) was introduced. BLE technology is mainly used because of the reduction of material and energy cost over time that has huge impact compared to other technology which is more costly. In this research, we propose a new hybrid method to increase the accuracy of indoor positioning system using hybrid of BLE fingerprinting and PDR. Kalman Filter is used as benchmark of this experiment and ANN and SVR are proposed as a new method to combine both measurements. On benchmark, Kalman filter achieve positioning root-mean-squared error 212.21 cm and the proposed SVR 149.12 cm and ANN 111.78 cm. CO 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

引用

页码：7101 / 7110

页数：10

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