关键词: 光通信, 可见光指纹定位, 高斯拟合, 卡尔曼滤波法, 加权K近邻法, 贝叶斯算法

Abstract: In view of the problem that indoor ambient light, noise and other factors will interfere with the intensity of the visible light signal strength received by the mobile terminal and cause the positioning accuracy to be low, this paper proposes a visible light fingerprint positioning method that integrates Gaussian fitting and Kalman filtering (GF-KF) with Improved-Bayes. Firstly, the RSS date collected by GF-KF algorithm is corrected as fingerprint database data, and then the weight coefficient of the k-neighbor method is transformed and fused with Bayesian algorithm, which matches the RSS data of the point to be measured and the fingerprint point Finally, the position is calculated. Experimental results show that under the algorithm model, the average positioning error is 1.42 cm, and 92.83% of the test point positioning error is not more than 2 cm, which is more accurate and robust than the convolutional neural network algorithm, the weighted K nearest neighbor algorithm and the support vector machine method.

Key words: optical communication, visible light positioning, Gaussian fitting, Kalman filtering, weighted K nearest neighbor method, Bayes algorithm