## 1 Introduction
Second, the Kalman filter used in Reference [9] simply combines the outcome of optical flow and Bayesian forecast without considering the state noise and measurement noise of the whole system, which greatly affects the accuracy of the VLP system. In this article, the unscented Kalman filter is introduced to improve the robustness and accuracy of our visible light positioning method and process the noises of the whole VLP system in a clever way. In Reference [26], the author proved that the introduction of the unscented Kalman filter into the camshift algorithm can improve the accuracy of the target tracking algorithm as same as reducing the running time of the whole system by leaps and bounds. Inspired by Reference [26], our proposed algorithm also used unscented Kalman filter (UKF) to improve the performance of the mean shift algorithm, and eventually our VLP method. However, though the unscented Kalman Filter in Reference [26] took noises into account, the noises were only Gaussian white noises, which are random noises without considering the condition of the target. If the LED is not occluded and the measured position of the LED is accurate, the noises considered will reduce the accuracy. Therefore, inspired by Chaos theory, a weight measuring the accuracy of the measured LED's position was introduced into our algorithm. If the accuracy of the measured LED's position is reliable, the weight of Gaussian white noises will be small, and the measured position will be trusted more by the Kalman filter. Through this method, the bad influence of using Gaussian white noise as the state noise and measurement noise can be reduced. To address the shortcomings of existing methods and improve the performance of our previous VLP system, in this paper, we propose a high-precision, real-time and robust indoor visible light positioning method based on the mean shift algorithm and unscented Kalman filter. The function of the LED is to deliver world coordinates using visible light. After the initial position of the positioning terminal is calculated by the LED-ID recognition algorithm, the mean shift algorithm is utilized to track and locate the LED in the image sequences in real time with high robustness. Then the trajectory of the positioning terminal can be calculated combining the positioning terminal's initial position with its relative position relationship in the subsequent frames calculated by MS and UKF. The UKF algorithm forecasts the possible location of the LED in the next frame, equipping the mean shift algorithm with the ability to track fast moving targets and reduces the computation cost of the whole algorithm. Also,when the LED is shielded, the trajectory of the positioning terminal will be output by UKF to reduce the influence of noises. Previous studies have considerable drawbacks in robustness because they had to repeat the complex positioning algorithm to get the trajectory of the positioning terminal. However, in our VLP method, only the initial world position of the positioning terminal is calculated by the static positioning algorithm. The trajectory can be calculated using the relative position relationship between the LED and the positioning terminal in the image sequence, which can be accomplished by the target tracking algorithm. Most of the repeating process of the static positioning algorithm is replaced by robust and lightweight target tracking algorithm. The remainder of this paper is organized as follows: Section 2 provides a detailed description of the proposed positioning and tracking algorithm. The experimental setup and analysis are presented in Section 3. Eventually, we summarize our work in Section 4.