The performance of MUSIC in white noise with limited samples and missing data
Raj Tejas Suryaprakash, Raj Rao Nadakuditi · 2014
The Multiple Signal Classification (MUSIC) algorithm is widely used to estimate the direction of arrival (DOA) of signals impinging on a sensor array. In this work, we analyze the performance of the MUSIC algorithm in the presence of white noise, and when only a random, sample independent subset of the entries in the data matrix are observed, in both the sample rich and deficient regimes. We derive a simple, closed form expression for the mean-squared-error (MSE) performance of MUSIC for a single source system, in the asymptotic regime and validate our analysis with simulations.