This paper proposes a high-precision and robust visible-light dynamic imaging positioning system based on improved kernelized correlation filters (IKCF) and fast-weighted Levenberg–Marquardt (FWLM) algorithm. First, using IKCF, we propose a novel LED-ID dynamic recognition algorithm, which can achieve fast LED-region of interest detection, and an adaptive model update mechanism is used to reduce the interference of occlusion. Second, an FWLM positioning algorithm is designed. The direct linear transformation is used to obtain the initial estimate of the pose quickly, and LM is employed to optimize the pose iteratively by minimizing the reprojection error. An adaptive weighting matrix is used to reduce the impact of LEDs with low detection reliability on positioning accuracy. Finally, an experimental platform is set up to verify the effectiveness of the proposed dynamic positioning algorithm. The experimental results show that the average positioning time is 4.08 ms, the average positioning accuracy is 1.9830 cm without occlusion, and the average positioning accuracy is 4.4024 cm with occlusion.
KEYWORDS: Light emitting diodes, Receivers, Telecommunications, Signal to noise ratio, 3D modeling, Visible radiation, Bacteria, Received signal strength, Optical engineering, Optimization (mathematics)
Visible light positioning has a good application prospect because of its simultaneous lighting, convenience, and security. However, most existing visible light communication (VLC) positioning systems cannot achieve real three-dimensional (3-D) positioning but just the small range approximation or fail to provide satisfactory positioning precision and speed. We propose a 3-D indoor localization system based on VLC using improved bacterial colony chemotaxis algorithm. The positioning problem is transformed into a global optimization problem. We reduce the dimensions of the positioning algorithm from 3-D to one-dimensional and propose a fitness function based on the simple geometric relationship between light-emitting diodes’ projection circles in the horizontal plane. In addition, we improve the traditional bacterial colony chemotaxis algorithm by adopting self-adaptive reception scope to improve global convergence and introducing differential evolution operator to overcome the algorithm premature. Our simulation results show that the mean positioning error is 0.73 mm in an indoor space of 5 m × 5 m × 6 m and the positioning time for a single point is 21.8 ms. Also, the positioning results show the advantages of the proposed positioning algorithm with high precision and high speed. The proposed positioning algorithm is practical and efficient, showing great application prospects in indoor positioning.
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