Statistical analysis of MUSIC and subspace rotation estimates of sinusoidal frequencies

Abstract

Consideration is given to 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 derived. These expressions of covariances are then used to analyze and compare the statistical performances of the 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 are used to study the dependence of MUSIC and SURE performances on the dimension of the data covariance matrix used.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>