Direction of arrival estimation using stochastic model order reduction via state space modeling

Abstract

In this paper, we present a class of high resolution direction finding techniques for processing of passive, uniform linear arrays. These are based on the state space parameterization of a linear array data. Directions of arrival (DOAs) are obtained as the eigenvalues of the state transition matrix φ. Since there exist an infinite number of state variable characterizations for a model, a number of possible methods can be devised depending on the manner in which the state transition matrix is identified. These techniques are also applicable to the situation of source coherence in array processing problems, which have required special attention in the past. Simulation results ascertaining the performance of these methods are also presented.