A Causal Temporal Convolutional Network for End-to-End Pose Estimation in Visible Light Positioning System

Abstract

Visible Light Positioning (VLP) has emerged as a promising solution for high-precision indoor localization due to its immunity to electromagnetic interference, high spatial resolution, and integration with existing lighting infrastructure. However, conventional Received Signal Strength (RSS)-based localization approaches suffer from severe performance degradation under nonlinear optical channel conditions, measurement noise, and orientation-dependent signal variations. This paper proposes a novel end-to-end causal Temporal Convolutional Network (TCN) framework for simultaneous three-dimensional position and single-axis orientation (azimuth) estimation for receivers moving on a fixed horizontal plane in indoor VLP systems. Unlike conventional Extended Kalman Filter (EKF)-based localization methods that rely on first-order linearization of nonlinear Lambertian channel models, the proposed TCN exploits causal dilated convolutions to capture temporal RSS dynamics and nonlinear mobility patterns directly from sequential measurements. A soft-attention temporal pooling mechanism is further incorporated to suppress noisy and unreliable RSS observations. The proposed framework is evaluated in a realistic simulated indoor VLP environment with 180,000 training samples generated under additive white Gaussian noise, background illumination interference, and optical crosstalk conditions. Simulation results demonstrate that the proposed TCN framework significantly outperforms the conventional EKF approach in terms of convergence speed, positioning accuracy, and tracking stability. The proposed method achieves convergence within approximately 2-3 iterations, whereas the EKF requires nearly 8-10 iterations under identical conditions. Furthermore, the average convergence time is reduced by approximately 59% while maintaining stable steady-state estimation performance. Experimental results also show lower position and orientation estimation errors, reduced localization outliers, and improved trajectory tracking accuracy during dynamic circular motion scenarios. The proposed TCN-based VLP framework provides a computationally efficient and robust solution for practical next-generation indoor localization applications.