A Comprehensive Survey on Subspace-based Direction of Arrival Estimation Algorithms: Performance, Accuracy and Complexity Analysis

Direction of Arrival (DOA) estimation is a critical problem in array signal processing, with applications in radar, wireless communications, and sonar systems. Subspace-based algorithms, such as MUSIC, R-MUSIC, and ESPRIT, have gained prominence due to their high-resolution capabilities and robustness against noise. This paper presents a comprehensive survey of these algorithms, focusing on their theoretical foundations, computational complexity, and estimation accuracy under different Signal-to-Noise Ratio (SNR) conditions. A comparative analysis is performed using a uniform linear array (ULA) with 30 elements and 500 snapshots, evaluating the root mean square error (RMSE) and computational operations required for each algorithm. Results indicate that R-MUSIC achieves the highest accuracy at the cost of increased complexity, while ESPRIT offers significant computational efficiency with slightly reduced accuracy. The study offers insights into algorithm selection for real-world applications, striking a balance between precision and computational feasibility in subspace-based DOA estimation techniques.