<![CDATA[Visible Light (VL) has received significant attention due to its benefits in energy efficiency, wide bandwidth availability, and resistance to electromagnetic interference. This final project discusses VL utilizing the Kalman Filter (KF) to predict and estimate the position of related data. The development of the VL method is carried out using Xilinx FPGA Arty A7 hardware, and the KF implementation is carried out in a two-dimensional framework with the Linear KF approach. The main objective of this Final Project is to implement VL using Photodetectors (Photodiode and Photoresistor LM393) on FPGA. The use of Xilinx FPGA Arty A7 hardware and Xilinx SDK software provides the flexibility and reliability required for system implementation. The results indicate that the implementation of Xilinx FPGA Arty A7-35T with KF and the use of 16 LED and 8 LED configurations yield relatively accurate estimations. While the Photodiode LM393 (PD LM393) sensor does not exhibit superior results compared to the Photoresistor LM393 (PR LM393) sensor, this research effectively optimizes light measurements by utilizing the sensor and KF algorithm. The Root Mean Squared Error (RMSE) results show that for the system with 16 LEDs, KF with PR LM393 has an RMSE of approximately ). This RMSE value indicates that KF with PR LM393 can provide relatively more accurate estimations. Similarly, for the system with 8 LEDs, KF with PR LM393 has an RMSE of around ). In this case, KF with PR LM393 again provides relatively more accurate estimations. Meanwhile, the RMSE result for 2D KF in this system is approximately ), indicating that the KF estimation has a relatively small error value compared to the actual measurement value. This demonstrates that KF effectively reduces noise and measurement data fluctuations in the LM393 Photodetector system with 16 LEDs.]]>
