Performance Evaluation of Deep Learning Approaches for Angle Estimation Based on AoA and DoA Estimation

Accurate Angle of Arrival (AoA) and Direction of Arrival (DoA) estimation is crucial for Indoor Positioning Systems (IPS). Traditional signal processing methods, such as MUSIC and ESPRIT, provide high-resolution estimation at the cost of high computational complexity and sensitivity to multipath interference. To address these limitations, deep learning-based approaches have been explored. However, a comprehensive evaluation under standardized conditions is still lacking. This study systematically analyzes AI-based angle estimation models by categorizing them according to data structure, representation format of data, and model architecture. A unified dataset and evaluation framework are employed to ensure fair performance comparison across different methods. The analysis investigates how various signal representations influence estimation accuracy and model behavior, particularly in handling environmental noise and signal distortions. By providing an objective performance assessment, this study offers insights into the applicability of deep learning models for IPS, contributing to the development of more robust and efficient angle estimation techniques.