An improved particle filter algorithm based on Cam-Shift algorithm applied to outdoor visible light communication (outdoor-VLC) is presented in this paper. In the outdoor-VLC system, accurately and completely tracking and extracting the target signal source Light Emitting Diode (LED) area is the premise for realizing communication. However, few existing studies pay attention to it. In the dynamic outdoor environment, there will be a lot of different environmental interferences (such as background interference, similar object interference, etc.), which greatly increases the difficulty of tracking and extracting the target signal source LED area. Therefore, in this paper, an improved tracking algorithm is proposed to solve the problem of how to track and extract the target signal source LED area accurately, stably and in real time in the outdoor-VLC system under various environmental interferences, so as to increase the feasibility of practical application of VLC system in outdoor scenes. This improved algorithm combines the particle filter algorithm and Cam-Shift algorithm originally. Experimental results show that the proposed algorithm has good accuracy, robustness and real-time performance under the environment of multiple interference factors. Accordingly, the proposed algorithm can be applied to the outdoor-VLC system with various environmental interferences, and can realize the actual first step of communication in VLC system based on image sensor well, laying a foundation for feature extraction, data transmission and other subsequent steps.

中文翻译：

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基于Cam-Shift算法的改进粒子滤波算法基于室外可见光通信的改进目标信号源跟踪提取方法

本文提出了一种基于Cam-Shift算法的改进粒子滤波算法应用于室外可见光通信(outdoor-VLC)。在户外VLC系统中，准确、完整地跟踪和提取目标信号源发光二极管（LED）区域是实现通信的前提。然而，现有研究很少关注它。在动态的室外环境中，会出现很多不同的环境干扰（如背景干扰、相似物体干扰等），大大增加了跟踪提取目标信号源LED区域的难度。因此，本文提出了一种改进的跟踪算法，以解决如何准确跟踪提取目标信号源LED区域的问题，在各种环境干扰下，在室外VLC系统中稳定实时，增加VLC系统在室外场景实际应用的可行性。该改进算法最初结合了粒子滤波算法和Cam-Shift算法。实验结果表明，该算法在多干扰因素环境下具有良好的准确性、鲁棒性和实时性。因此，该算法可以应用于各种环境干扰的室外VLC系统，可以很好地实现基于图像传感器的VLC系统中实际通信的第一步，为特征提取、数据传输等后续步骤奠定基础。 . 从而增加VLC系统在户外场景实际应用的可行性。该改进算法最初结合了粒子滤波算法和Cam-Shift算法。实验结果表明，该算法在多干扰因素环境下具有良好的准确性、鲁棒性和实时性。因此，该算法可以应用于各种环境干扰的室外VLC系统，可以很好地实现基于图像传感器的VLC系统中实际通信的第一步，为特征提取、数据传输等后续步骤奠定基础。 . 从而增加VLC系统在户外场景实际应用的可行性。该改进算法最初结合了粒子滤波算法和Cam-Shift算法。实验结果表明，该算法在多干扰因素环境下具有良好的准确性、鲁棒性和实时性。因此，该算法可以应用于各种环境干扰的室外VLC系统，可以很好地实现基于图像传感器的VLC系统中实际通信的第一步，为特征提取、数据传输等后续步骤奠定基础。 . 实验结果表明，该算法在多干扰因素环境下具有良好的准确性、鲁棒性和实时性。因此，该算法可以应用于各种环境干扰的室外VLC系统，可以很好地实现基于图像传感器的VLC系统中实际通信的第一步，为特征提取、数据传输等后续步骤奠定基础。 . 实验结果表明，该算法在多干扰因素环境下具有良好的准确性、鲁棒性和实时性。因此，该算法可以应用于各种环境干扰的室外VLC系统，可以很好地实现基于图像传感器的VLC系统中实际通信的第一步，为特征提取、数据传输等后续步骤奠定基础。 .