Simultaneous Estimation of Azimuth and Elevation Angles Using a Decision Tree-Based Method

This study addresses the problem of accurately predicting azimuth and elevation angles signals impinging on an antenna array employing Machine Learning (ML). Using information obtained at a receiving system when transmitter’s signal hits it, Decision Tree (DT) model is trained to estimate simultaneously. Simulation results demonstrate robustness proposed DT-based method, showcasing its ability predict Direction Arrival (DOA) in diverse conditions beyond ones present training dataset, i.e., display model’s generalization capability. Additionally, comparative analysis reveals that DOA estimation outperforms state-of-the-art MUltiple SIgnal Classification (MUSIC) algorithm. Our average reduction over 90% prediction error 50% time achieved by our proposal compared MUSIC These establish DTs as competitive alternatives for reception systems.