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Artificial Neural Network Based Indoor Positioning in Visible Light Communication Systems

机译:可见光通信系统中基于人工神经网络的室内定位

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In this study, an artificial neural network (ANN) based visible light positioning system is proposed. For this purpose, an empty and closed room scenario is considered. In this scenario, a LED (light emitting diode) bulb is the transmitter and a photodiode is the receiver. The communication channel is modelled as a visible light channel which take into account of multipath reflections. In this regard, Combined Deterministic and Modified Monte Carlo (CDMMC) method is used. Then the proposed system and the RSS-based system in the literature are compared with each other in terms of positioning performances in indoor environment. The RSS-based indoor positioning system have high positioning errors due to the internal reflections into the room. As a result of this study, the ANN-based positioning system has much higher accuracy and near-realistic results than the RSS-based positioning system has. The disturbing effects of multipath reflections on localization has been considerably removed using ANN-based methods.
机译:在这项研究中,提出了一种基于人工神经网络的可见光定位系统。为此,考虑一个空的和封闭的房间方案。在这种情况下,LED(发光二极管)灯泡是发送器,光电二极管是接收器。将通信通道建模为考虑多径反射的可见光通道。在这方面,使用了确定性和改进的蒙特卡洛组合(CDMMC)方法。然后将本文提出的系统与基于RSS的系统在室内环境下的定位性能进行比较。基于RSS的室内定位系统由于对房间的内部反射而具有很高的定位误差。这项研究的结果是,与基于RSS的定位系统相比,基于ANN的定位系统具有更高的准确性和接近真实的结果。使用基于ANN的方法已大大消除了多径反射对定位的干扰作用。

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