The performance of music-based DOA in white noise with missing data

The Multiple Signal Classification (MUSIC) algorithm is popular choice for estimating the direction of arrival (DOA) of signals impinging on a sensor array. In this paper, we analyze the mean-squared error (MSE) performance of MUSIC algorithm in the white noise setting with partially observed, or missing, data. Using recent results from random matrix theory, we obtain an analytic expression for the MSE of the DOA estimate in the asymptotic regime and validate the theoretical predictions with simulations.