Probability Density Functions of the Subspace-Based Direction of Arrival Estimators.

MUSIC, MNM, and ESPRIT algorithms are commonly used subspace-based estimators for estimating directions of arrival of plane waves using the data received by a passive sensor array. The estimators perform differently in three different SNR ranges, namely no information region, low information region, and high information region. We evaluate and analyze the PDFs of the subspace-based estimators for fully populated and sparse linear arrays and uniform circular arrays in the three SNR ranges. The simulations demonstrate that in the high information region, the PDFs of the subspacebased estimators resemble Gaussian distributions whose expected values are equal to the true parameter values and variances are small. The PDFs are non-uniform and asymmetric in the low information region.