Kucuk, Mehmet Ali; Turk, Kadir

In this letter, an angle-of-arrival (AoA)-based integrated positioning system is proposed that utilizes the standard optical front-end of multi-color visible light communication (MC-VLC). Unlike traditional AoA methods, which rely on specialized receiver architectures, the proposed approach uses the inherent angular dependence of optical filter transmission responses, known as the blue-shift effect, for directly estimating the angle of incidence from the received signal. In this context, an AoA estimation technique is developed by combining eigenvalue decomposition of the MC-VLC channel with regression analysis. Simulation results show that the proposed system achieves millimeter-level positioning accuracy, enabling robust and high-precision indoor localization without additional hardware complexity or cost.

Cetin, Gokhan; Kucuk, Mehmet Ali; Koroglu, Muhammed Taha

Traditional pedestrian dead-reckoning (DR) methods utilizing inertial measurement units (IMU) have been successfully employed in GPS-denied environments over the past two decades. However, despite continuous advancements in wearable IMU technology, the long-term solution still suffers from the issue of drift. In light of this, Deep Learning (DL) has emerged as a potential candidate for inertial navigation solutions, leveraging its powerful non-linear regression capabilities. Recent research has demonstrated that DL models trained on public datasets can effectively serve as end-to-end solutions. Most studies focus on smartphones, where the IMU is typically situated at the torso, and generally work with transformed data represented in the navigation coordinate frame to estimate pedestrian velocity and heading change at rates lower than the IMU sampling rate. In contrast, this study uses raw foot-mounted IMU data as input to a multi-headed structure consisting of independent convolutional neural networks (CNNs) and targets displacement directly and heading change. The proposed model is trained on a publicly available foot-mounted IMU dataset where pseudo ground-truth (GT) data is generated heuristically to provide supervision for the learning process. The results indicate that the Multi-Head CNN (MHCNN) model is capable of producing pedestrian trajectories at the IMU sampling rate, with positioning errors of less than 2m throughout the duration of the walks. The code for the trained network can be accessed in the associated repository, i.e., github.com/mtkoroglu/PIN-MHCNN.