Performance Evaluation of Direction of Arrival Estimation in Uniform Circular Arrays Based on the Root-Music Algorithm

Abstract

This study evaluates the performance of the Root-MUSIC algorithm for Direction of Arrival (DOA) estimation in Uniform Circular Arrays (UCAs). While Root-MUSIC is widely recognized for its high resolution and computational efficiency, its application in UCAs has been less explored. This research investigates the accuracy, robustness, and computational efficiency of the algorithm under various conditions, including different array sizes (N = 8, 16, 20, 26), signal-to-noise ratios (SNRs from 2 to 5 dB), and the presence of array imperfections such as mutual coupling, non-uniform element spacing, and calibration errors. The results show that increasing the array size (N) improves DOA resolution, with N = 26 achieving the best accuracy but at a higher computational cost. However, array imperfections were found to degrade the accuracy, highlighting the need for calibration techniques to address these errors. A comparative analysis with MUSIC (non-root) and ESPRIT demonstrates that Root-MUSIC strikes a balance between accuracy and efficiency, although ESPRIT is computationally more efficient and better suited for real-time applications. Computational complexity analysis confirms that Root-MUSIC, despite its high precision, requires increased processing time as the array size grows, which may limit its practical use in large-scale or time-sensitive systems. This study provides a deeper understanding of the performance of Root-MUSIC in UCAs and offers insights into its potential applications in radar, wireless communication, and sensor networks. Future work should focus on optimizing Root-MUSIC for real-time applications, exploring hybrid DOA estimation techniques, and validating results with real-world data.