Performance Evaluation of 2-D DOA Estimation Algorithms in Noisy Channels

In this paper, an overview of three well-known two-dimensional Direction Of Arrival (DOA) estimation algorithms, namely, MUltiple SIgnal Classification (MUSIC), Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) and Propagator Method (PM) is presented. In order to reduce the computational complexity of 2-D methods, azimuth and elevation estimations are extracted from two one-dimensional estimations. As the main objective of this investigation, considering 1-D realization of 2-D DOA estimation algorithms and simulation them in MATLAB software, the Root Mean Square Error (RMSE) performance of these methods is compared in three cases, uncorrelated, correlated and coherent signals in the presence of white Gaussian noise as well as colored noise. Simulation results show that for uncorrelated signals, MUSIC in low Signal to Noise Ratios (SNRs) and ESPRIT in high SNRs offer lower RMSE. In the case of coherent and correlated signals, ESPRIT is the best choice in all SNRs. Finally, for colored noise scenario, PM provides more accurate estimation for low SNRs, while ESPRIT has a lower RMSE for high SNRs compared to two other methods.