AI-Enhanced Sensor Fusion: Bridging Traditional and Neural Network Filters in Real-Time Localization

In the context of digital twins and industrial IoT systems, robust sensor fusion is essential for accurate real-time localization. This paper presents a comparative evaluation of three sensor fusion strategies for indoor 2D localization: a classical Extended Kalman Filter (EKF), a hybrid architecture combining the filter with a Recurrent Neural Network (RNN) for error compensation, and a pure AI model that learns the fusion process directly from multi-sensor sequences. Experimental results show that the hybrid approach consistently achieves the best trade-off between accuracy and real-time responsiveness, outperforming both the classical and the purely AI-based alternative.