Approximated stochastic realization and model reduction methods applied to array processing by means of state space models

The aim of this study is to present novel methods for passive array processing. The basic idea consists in using state-space modeling of the sensors´ output. The authors deal with basic problems such as unknown noise correlations, approximation by a Toeplitz matrix of lower rank, and detection of small sources. The methods presented represent considerable improvements with respect to the usual methods and furthermore are quite feasible. Some statistical results illustrate these claims