In order to address the difficult fingerprinting-based visible light positioning (VLP) issue,an efficient fin-gerprinting interference model (FIM) calibration algorithm is proposed via leveraging Bayesian inference and stochastic op-timization approaches. Firstly,a fingerprinting database is built by collecting received signal strength of visible light from various observation grids with known location and pose angles. Secondly,a Gaussian-form FIM is developed as per maxi-mum entropy theory,and then FIM calibration is treated as a stochastic optimization problem. Finally,a successive convex approximation-driven optimization algorithm is proposed for calibrating FIM parameters by exploiting hidden convex sub-structures of FIM,thus improving the fingerprinting-based VLP performance. With our problem-specific algorithm design,the proposed FIM calibration-enhanced VLP method can alleviate the disturbance from non-line-of-sight propagation inter-ference and fingerprinting model mismatch. It is verified by simulation results that our FIM calibration-enhanced VLP meth-od outperforms the state-of-the-art baseline methods.
维普
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