Iterative point-wise reinforcement learning for highly accurate indoor visible light positioning
Artikel i vetenskaplig tidskrift, 2019

Iterative point-wise reinforcement learning (IPWRL) is proposed for highly accurate indoor visible light positioning (VLP). By properly updating the height information in an iterative fashion, the IPWRL not only effectively mitigates the impact of non-deterministic noise but also exhibits excellent tolerance to deterministic errors caused by the inaccurate a priori height information. The principle of the IPWRL is explained, and the performance of the IPWRL is experimentally evaluated in a received signal strength (RSS) based VLP system and compared with other positioning algorithms, including the conventional RSS algorithm, the k-nearest neighbors (KNN) algorithm and the PWRL algorithm where iterations exclude. Unlike the supervised machine learning method, e.g., the KNN, whose performance is highly dependent on the training process, the proposed IPWRL does not require training and demonstrates robust positioning performance for the entire tested area. Experimental results also show that when a large height information mismatch occurs, the IPWRL is able to first correct the height information and then offers robust positioning results with a rather low positioning error, while the positioning errors caused by the other algorithms are significantly higher.


Zhuo Zhang

South China Normal University

Yaguang Zhu

South China Normal University

Wentao Zhu

South China Normal University

Huayang Chen

South China Normal University

Xuezhi Hong

South China Normal University

Jiajia Chen

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Optiska nätverk

South China Normal University

Kungliga Tekniska Högskolan (KTH)

Optics Express

1094-4087 (ISSN)

Vol. 27 16 22161-22172


Medicinsk laboratorie- och mätteknik


Datorseende och robotik (autonoma system)



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