Visible light communication (VLC) systems are promising candidates for future indoor access and peer-to-peer networks. The performance of these systems, however, is vulnerable to line of sight (LOS) link blockage due to objects inside the room. Considering pedestrians as the most common VLC links blocking obstacles, we develop a probabilistic passive pedestrian detection and localization method. Our method takes advantage of the blockage status of VLC LOS links between the user equipment (UE) and transceivers on the ceiling to passively detect a single pedestrian, modeled as a cylinder with a random radius. The VLC network gathers the blockage status and computes the geometry of the LOS link graph through a cooperative scheme between VLC device-equipped users inside the room. We also develop a mathematical framework to obtain an optimum solution for estimating the location and size of the object and conclude with a sub-optimum estimation by simplifying the problem to a quadratic programming approach. Simulation results show that using a $5 \times 5$ grid of transceivers on the ceiling and as few as eight UEs, the root-mean-squared error in estimating the center and radius of the object can be less than 5 cm and 3 cm, respectively.

中文翻译：

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使用可见光通信访问网络的协作式被动行人检测和定位

可见光通信（VLC）系统是未来室内访问和对等网络的有希望的候选者。但是，由于房间内的物体，这些系统的性能容易受到视线（LOS）链路阻塞的影响。考虑到行人是阻挡障碍物的最常见的VLC链接，我们开发了一种概率性被动行人检测和定位方法。我们的方法利用了用户设备（UE）与天花板上的收发器之间的VLC LOS链接的阻塞状态，以被动地检测建模为具有随机半径的圆柱体的单个行人。VLC网络通过房间内配备VLC设备的用户之间的协作方案，收集阻塞状态并计算LOS链接图的几何形状。我们还开发了一个数学框架，以获得用于估计对象的位置和大小的最佳解决方案，并通过将问题简化为二次编程方法来得出次优估计。仿真结果表明，使用 $ 5 \次5 $ 天花板上的收发器网格以及多达八个UE，估计对象中心和半径的均方根误差分别可以小于5 cm和3 cm。