The Light Positioning System (LPS) represents an innovative technology employed for precise object localization by utilizing light as a positional reference. This method encompasses the utilization of light sources, such as LED lights or other visible light emitters, which can be strategically positioned at various orientations and angles. This research centers on the practical implementation of the LPS paradigm through the application of Arduino. Additionally, the study involves the integration of the Kalman filter algorithm within the Arduino framework to enhance the accuracy of sensor data estimations. The LPS implementation employs distinct sensors, namely the Photoresistor LM393, Photodiode LM393, and TF-Luna Lidar. The programming is accomplished using the Arduino Integrated Development Environment (IDE), while the hardware framework is based on the Arduino Mega 2560 microcontroller. In this research, the ESP32 module plays a pivotal role as it establishes a seamless connection between the sensor data and the Blynk platform. This integration empowers effective and comprehensive data monitoring and analysis, facilitating real-time tracking and evaluation of the LPS system's performance. The photoresistor exhibits better reading accuracy compared to the photodiode, as evident from the obtained RMSE values. The KF PR with 16 LEDs has the smallest RMSE value, which is 0,03. The TF-Luna LiDAR sensor readings are more accurate and effective under well-lit conditions as opposed to low-light conditions. The RMSE value at lux 160 is 1,28 , while the RMSE value at lux 2 is 3,3
