A neural network-based visible light communication indoor positioning system for moving users /
Wafaa Sayed Ebrahim Ahmed
A neural network-based visible light communication indoor positioning system for moving users / نظام تحديد المواقع داخل المبانى بإستعمال إتصالات الضوء المرئى و الشبكات العصبية الصناعية Wafaa Sayed Ebrahim Ahmed ; Supervised Khaled Mohamed Fouad Elsayed , Tawfik Ismail Tawfik - Cairo : Wafaa Sayed Ebrahim Ahmed , 2020 - 73 P . : charts , facsmilies ; 30cm
Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Electronics and Communications
In this thesis, we present an indoor visible light communication (VLC) system to estimate the position of a moving user. This system uses two approaches based on received signal strength, trilateration estimation and neural network estimation. In VLC system, each transmitter sends its position information via light. A photo-detector receiver supported with the moving user is used to receive the transmitted power from each transmitter. The position of the receiver is calculated by using trilateration estimation and neural network estimation. In our study, we consider the two cases, the case of line of sight (LOS) and Non-Line of Sight (NLOS). In case of the receiver normal is not parallel to the transmitter normal, the results showed that trilateration approach gives an error of 18 cm (94% accuracy) while, Neural Network approach offers more accurate positioning with 14cm error (95.3% accuracy) for trained data and 16 cm error (94.6% accuracy) for untrained data.
Indoor positioning Received signal strength Visible light communication
A neural network-based visible light communication indoor positioning system for moving users / نظام تحديد المواقع داخل المبانى بإستعمال إتصالات الضوء المرئى و الشبكات العصبية الصناعية Wafaa Sayed Ebrahim Ahmed ; Supervised Khaled Mohamed Fouad Elsayed , Tawfik Ismail Tawfik - Cairo : Wafaa Sayed Ebrahim Ahmed , 2020 - 73 P . : charts , facsmilies ; 30cm
Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Electronics and Communications
In this thesis, we present an indoor visible light communication (VLC) system to estimate the position of a moving user. This system uses two approaches based on received signal strength, trilateration estimation and neural network estimation. In VLC system, each transmitter sends its position information via light. A photo-detector receiver supported with the moving user is used to receive the transmitted power from each transmitter. The position of the receiver is calculated by using trilateration estimation and neural network estimation. In our study, we consider the two cases, the case of line of sight (LOS) and Non-Line of Sight (NLOS). In case of the receiver normal is not parallel to the transmitter normal, the results showed that trilateration approach gives an error of 18 cm (94% accuracy) while, Neural Network approach offers more accurate positioning with 14cm error (95.3% accuracy) for trained data and 16 cm error (94.6% accuracy) for untrained data.
Indoor positioning Received signal strength Visible light communication