STATISTICAL-ANALYSIS OF MUSIC AND SUBSPACE ROTATION ESTIMATES OF SINUSOIDAL FREQUENCIES

This paper is concerned with the analysis of the large-sample second-order properties of multiple signal classification (MUSIC) and subspace rotation (SUR) methods, such as ESPRIT, for sinusoidal frequency estimation. Explicit expressions for the covariance elements of the estimation errors associated with either method are first derived. These expressions of covariances are then used to analyze and compare the statistical performances of MUSIC and SUR estimation (SURE) methods. Both MUSIC and SURE are based on the eigendecomposition of a sample data covariance matrix. The expressions for the estimation error variances derived in this paper are also used to study the dependence of MUSIC and SURE performances on the dimension or the data covariance matrix employed.