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Sensors (Basel). 2019 May 20;19(10). pii: E2324. doi: 10.3390/s19102324.

High-Precision Indoor Visible Light Positioning Using Modified Momentum Back Propagation Neural Network with Sparse Training Point.

Zhang H1,2, Cui J3,4, Feng L5,6, Yang A7,8, Lv H9,10, Lin B11, Huang H12.

Author information

1
Key Laboratory of Photonics Information Technology, Ministry of Industry and Information Technology, Beijing 100081, China. bitzhanghaiqi@sina.com.
2
School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China. bitzhanghaiqi@sina.com.
3
Key Laboratory of Photonics Information Technology, Ministry of Industry and Information Technology, Beijing 100081, China. jiahe.cui@eng.ox.ac.uk.
4
School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China. jiahe.cui@eng.ox.ac.uk.
5
Key Laboratory of Photonics Information Technology, Ministry of Industry and Information Technology, Beijing 100081, China. lihui.feng@bit.edu.cn.
6
School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China. lihui.feng@bit.edu.cn.
7
Key Laboratory of Photonics Information Technology, Ministry of Industry and Information Technology, Beijing 100081, China. yangaiying@bit.edu.cn.
8
School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China. yangaiying@bit.edu.cn.
9
Key Laboratory of Photonics Information Technology, Ministry of Industry and Information Technology, Beijing 100081, China. oncepursuit@gmail.com.
10
School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China. oncepursuit@gmail.com.
11
China Academy of Electronics and Information Technology, Beijing 100041, China. bolin_academic@163.com.
12
China Academy of Electronics and Information Technology, Beijing 100041, China. huangheqingbit@163.com.

Abstract

In this letter, we propose an indoor visible light positioning technique using a Modified Momentum Back-Propagation (MMBP) algorithm based on received signal strength (RSS) with sparse training data set. Unlike other neural network algorithms that require a large number of training data points to locate accurately, we have realized high-precision positioning for 100 test points with only 20 training points in a 1.8 m × 1.8 m × 2.1 m localization area. In order to verify the adaptability of the MMBP algorithm, we experimentally demonstrate two different training data acquisition methods adopting either even or arbitrary training sets. In addition, we also demonstrate the positioning accuracy of the traditional RSS algorithm. Experimental results show that the average localization accuracy optimized by our proposed algorithm is only 1.88 cm for the arbitrary set and 1.99 cm for the even set, while the average positioning error of the traditional RSS algorithm reaches 14.34 cm. Comparison indicates that the positioning accuracy of our proposed algorithm is 7.6 times higher. Results also show that the performance of our system is higher than some previous reports based on RSS and RSS fingerprint databases using complex machine learning algorithms trained by a large amount of training points.

KEYWORDS:

LED; high accuracy; indoor visible light positioning; neural network

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