RBF neural network indoor visible light positioning algorithm based on multi-source information fusion
Aiming at the problem of low positioning accuracy and poor stability caused by the environmental interference of the positioning technology based on received signal strength(RSS),an radial basis function(RBF)neural network indoor visible light positioning algorithm based on multi-source information fusion is proposed.By fusing the color moment feature of the im-age with the RSS moment feature,a fingerprint database is constructed,and the RBF neural network is used for prediction to achieve complementary advantages between the image and RSS.Finally,the positioning algorithm is verified.The experimental results show that the optimized multi-source information fusion positioning algorithm improves the positioning accuracy by 9.4%compared with the single RSS positioning algorithm.
visible lightindoor positionmulti-source information fusioncolor momentnneural networksradial basis functionfeature extraction
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