Direction Estimation Using Uniform Circular Arrays with Performance Analysis

This study implements direction-of-arrival (DOA) estimation using uniform circular arrays (UCA) through simulation of power spectrum patterns. The implementation involves array signal processing algorithms where sensor positions are calculated using circular geometry with radius R and M equally spaced elements. The steering vector for UCA is computed using mathematical functions that account for the circular arrangement, typically involving Bessel function expansions or phase mode excitation techniques. Comparative simulations with uniform linear arrays (ULA) demonstrate that UCAs provide better estimation performance due to their 360-degree azimuth coverage and improved angular resolution. The algorithm implementation includes covariance matrix calculation from received signals, followed by spectral estimation methods like MVDR (Minimum Variance Distortionless Response) or MUSIC (Multiple Signal Classification) adapted for circular geometry. The research further investigates UCA performance under varying conditions, including different signal-to-noise ratio (SNR) scenarios. The simulation code incorporates SNR adjustments through additive white Gaussian noise (AWG) injection and evaluates estimation accuracy using metrics like root mean square error (RMSE). These comparative analyses provide valuable references for future array signal processing research, particularly in applications requiring full azimuth coverage and robust direction finding capabilities.