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Visible light positioning (VLP) is a promising technology for indoor navigation. However, most studies of VLP systems nowadays only focus on positioning accuracy, whereas robustness and real-time ability are often overlooked, which are all indispensable in actual VLP situations. Thus, we propose a novel VLP method based on mean shift (MS) algorithm and unscented Kalman filter (UKF) using image sensors as the positioning terminal and a Light Emitting Diode (LED) as the transmitting terminal. The main part of our VLP method is the MS algorithm, realizing high positioning accuracy with good robustness. Besides, UKF equips the mean shift algorithm with the capacity to track high-speed targets and improves the positioning accuracy when the LED is shielded. Moreover, a LED-ID (the identification of the LED) recognition algorithm proposed in our previous work was utilized to locate the LED in the initial frame, which also initialized MS and UKF. Furthermore, experiments showed that the positioning accuracy of our VLP algorithm was 0.42 cm, and the average processing time per frame was 24.93 ms. Also, even when half of the LED was shielded, the accuracy was maintained at 1.41 cm. All these data demonstrate that our proposed algorithm has excellent accuracy, strong robustness, and good real-time ability.

An improved Cam-Shift algorithm with a Kalman filter applied to image-sensor based on outdoor visible light communication (OVLC) is presented in this paper. The proposed optimized tracking algorithm is used to track and extract the region of the target signal source Light Emitting Diode (LED) that carries modulated information for data transmission. Extracting the target signal source LED area is the premise of an image-sensor-based VLC system, especially in outdoor dynamic scenes. However, most of the existing VLC studies focus on data transmission rate, visible light positioning, etc. While the actual first step of realizing communication is usually ignored in the field of VLC, especially when the transmitter (signal source LED) or the receiver (image sensor) is moving in a more complex outdoor environment. Therefore, an improved tracking algorithm is proposed in this paper, aiming at solving the problem of extracting the region of the target signal source LED accurately in dynamic scenes with different interferences so as to promote the feasibility of VLC applications in outdoor scenes. The proposed algorithm considers color characteristics and special distribution characteristics of the moving target at the same time. The image is converted to a color probability distribution map based on the color histogram of the target and adaptively adjusts the location and size of the search window based on the results obtained from the previous frame. Meanwhile, it predicts the motion state of the target in the next frame according to the position and velocity information of the current frame to enhance accuracy and robustness of tracking. Experimental results show that the tracking error of the proposed algorithm is 0.85 cm and the computational time of processing one frame is 0.042 s. Besides, results also show that the improved algorithm can track and extract the target signal source LED area completely and accurately in an environment of many interference factors. This study confirms that the proposed algorithm can be applied to an OVLC system with many interferences to realize the actual first step of communication in an image-sensor-based VLC system, laying foundations for subsequent data transmission and other steps.

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