Multi-Horizon Direction of Arrival Forecasting with Temporal Fusion Transformers: A Time-Series Learning Approach to Target Tracking
Constantinos M. Mylonakis·Zaharias D. Zaharis·Panagiotis Sarigiannidis·Marco Di Renzo·Nikolaos V. Kantartzis·Pavlos I. Lazaridis·Sotirios K. Goudos
This paper explores the application of Temporal Fusion Transformers (TFTs) to target recognition and multi-horizon tracking, with a particular emphasis on enhancing Direction of Arrival (DoA) estimation in complex signal environments. By leveraging the advanced temporal modeling capabilities and dynamic feature selection mechanisms inherent to the TFT architecture, we adapt and optimize the model for improved performance in antenna signal processing, leading to a more accurate and robust estimat