Demonstration of three‐dimensional indoor visible light positioning with multiple photodiodes and reinforcement learning
Artikel i vetenskaplig tidskrift, 2020

To provide high‐quality location‐based services in the era of the Internet of Things, visible light positioning (VLP) is considered a promising technology for indoor positioning. In this paper, we study a multi‐photodiodes (multi‐PDs) three‐dimensional (3D) indoor VLP system enhanced by reinforcement learning (RL), which can realize accurate positioning in the 3D space without any off-line training. The basic 3D positioning model is introduced, where without height information of the receiver, the initial height value is first estimated by exploring its relationship with the received signal strength (RSS), and then, the coordinates of the other two dimensions (i.e., X and Y in the horizontal plane) are calculated via trilateration based on the RSS. Two different RL processes, namely RL1 and RL2, are devised to form two methods that further improve horizontal and vertical positioning accuracy, respectively. A combination of RL1 and RL2 as the third proposed method enhances the overall 3D positioning accuracy. The positioning performance of the four presented 3D positioning methods, including the basic model without RL (i.e., Benchmark) and three RL based methods that run on top of the basic model, is evaluated experimentally. Experimental results verify that obviously higher 3D positioning accuracy is achieved by implementing any proposed RL based methods compared with the benchmark. The best performance is obtained when using the third RL based method that runs RL2 and RL1 sequentially. For the testbed that emulates a typical office environment with a height difference between the receiver and the transmitter ranging from 140 cm to 200 cm, an average 3D positioning error of 2.6 cm is reached by the best RL method, demonstrating at least 20% improvement compared to the basic model without performing RL.

Visible light positioning

3D indoor positioning

Reinforcement learning

Författare

Zhuo Zhang

South China Normal University

Huayang Chen

South China Normal University

Weikang Zeng

South China Normal University

Xinlong Cao

South China Normal University

Xuezhi Hong

South China Normal University

Jiajia Chen

South China Normal University

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Optiska nätverk

Sensors

1424-8220 (ISSN) 1424-3210 (eISSN)

Vol. 20 22 1-14 6470

Ämneskategorier

Reglerteknik

Signalbehandling

Datorseende och robotik (autonoma system)

DOI

10.3390/s20226470

PubMed

33198393

Mer information

Senast uppdaterat

2020-12-01