Indoor NLOS-VLP System Based on Image Sensor and Pixel Coordinate Fingerprinting

Visible light positioning (VLP) has become one of the most important technologies for providing highly accurate location-based services. Despite this, line-of-sight VLP systems have severe limitations when operating in complex and dynamic indoor environments. We propose a non-line-of-sight (NLOS) VLP system based on an image sensor (IS) and pixel coordinate fingerprinting operating under shadowing and blocking. The system does not require communication between the transmitter and the receiver eliminating the need for a complex modulation process. For the first time, we utilize the highlighted center pixel coordinates on an IS as the fingerprint features and extract highlight center coordinates using the deep learning model Yolov8. Furthermore, we propose an Elastic Net regression with Weighted K-Nearest Neighbor Residual Correction algorithm to improve the positioning performance, which employs an Elastic Net for global prediction and a WKNN algorithm to correct the global prediction residuals by leveraging the residual information from weighted neighboring samples. The experimental results show that the average positioning error and 90th percentile errors of the proposed system are 4.77 and 6.67 cm, respectively, with only 16 training points. In addition, the stability of the system is verified by arbitrary and diagonal sets.