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  • Received: May. 8, 2020

    Accepted: Jun. 11, 2020

    Posted: Sep. 1, 2020

    Published Online: Sep. 2, 2020

    The Author Email: Wu Yi (, Wang Xufang (

    DOI: 10.3788/AOS202040.1806003

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    Shiwu Xu, Yi Wu, Xufang Wang. Visible Light Positioning Algorithm Based on Sparsity Adaptive and Location Fingerprinting[J]. Acta Optica Sinica, 2020, 40(18): 1806003

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Acta Optica Sinica, Vol. 40, Issue 18, 1806003 (2020)

Visible Light Positioning Algorithm Based on Sparsity Adaptive and Location Fingerprinting

Xu Shiwu1,2, Wu Yi1,*, and Wang Xufang1,**

Author Affiliations

  • 1Key Laboratory of Opto-Electronic Science and Technology for Medicine, Ministry of Education, Fujian Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian 350007, China
  • 2Concord University College, Fujian Normal University, Fuzhou, Fujian 350117, China


In this paper, a low-complexity, sparsity adaptive compressed sensing algorithm is proposed based on fingerprint localization of visible light communication. First, the localization problem is transformed into a sparse matrix reconstruction problem based on the sparsity of location fingerprints. Second, the nearest neighbor value is adaptively calculated based on the reconstructed residual value. Finally, the impact of fingerprint sampling interval, signal-to-noise ratio, modulation bandwidth, and transmission power on positioning errors are analyzed in detail. Moreover, the time complexity, distribution of the optimal nearest neighbor values, number of the light-emitting diodes, and maximum number of nearest neighbor fingerprints of the proposed positioning algorithm on positioning errors are also analyzed. The simulation results show that the proposed positioning algorithm has comparatively low average calculation time and small positioning error. When the signal-to-noise ratio and the distance between the fingerprints are 10 dB and 40 cm, respectively, the average positioning error of the proposed positioning algorithm is 1.56 cm, which is significantly lower than those of existing algorithms.


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