Subspace Perturbation Bounds with an Application to Angle of Arrival Estimation using the MUSIC Algorithm
Connor Delaosa, Jennifer Pestana, Stephan Weiss, Ian K. Proudler · 2020
This paper explores how angle of arrival (AoA) estimation using the multiple signal classification (MUSIC) algorithm is affected by estimation errors in the space-time covariance matrix. In particular, we explore how this estimation error perturbs the signal-plus-noise and noise-only subspaces of the matrix, and how this subsequently affects the performance of MUSIC for AoA estimation. This subspace perturbation is shown to depend on the space-time covariance matrix itself, the sample size over which it is estimated, as well as the distance of the smallest signal-related eigenvalue to the noise floor. We link a bound on this perturbation to a bound on MUSIC performance, and demonstrate its utility for AoA estimation in simulations.