Performance bounds for subspace estimation in array signal processing

Estimation of unknown parameters using arrays of passive sensors is a well-known problem in signal processing. In this paper, this problem studied via subspace estimation using geometric representations on Grassman manifolds. The variability on Grassman manifold is modeled by a transitive action of special unitary group and using a Bayesian formulation on the space of unitary matrices. A posterior is used to derive an MMSE, estimator and a lower-bound on the expected squared error. Empirical analysis using stochastic gradient processes are considered for numerical computation of the estimator and the lower bound.