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Indoor Visible Light Positioning Utilizing a Changed Frilled Lizard Optimization Algorithm
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AI
DOI：10.1007/s13369-026-11146-9.png)
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Abstract
En 中文
In indoor visible light positioning systems, the random rotation of the photodetector (PD) reduces positioning accuracy, yet this issue is often overlooked in existing research. This paper, therefore, proposes a positioning scheme based on a changed frilled lizard optimization (CFLO) algorithm to address this issue. Integrating Chebyshev chaotic mapping, adaptive t-distribution mutation, and a lens imaging inverse learning mechanism significantly enhances the algorithm’s global exploration capabilities and convergence stability. Simulation results show that, in complex scenarios involving heights ranging from 1 to 3 m, the proposed algorithm rapidly converges when the PD rotates randomly between 0 and 15° around the X/Y axes and between 0 and 360° around the Z-axis. The 3D error of 90% of the positioning points is controlled to within 29.476 cm. Compared to a positioning error of 47.392 cm when rotation is ignored, the system achieves significantly improved accuracy. This study confirms the necessity of fully accounting for detector rotation in positioning models and validates the CFLO algorithm’s feasibility and effectiveness in solving this problem. It provides a new approach for advancing indoor visible light positioning technology.
Keywords:
Photodetector rotation
Visible light localization
Received signal strength
Changed frilled lizard optimization
Meta-heuristic algorithms
Positioning accuracy
Journal
A
IF:
2.9
Papers:
962
Citations:
0