Optimal Subspace Techniques for DOA Estimation in the Presence of Noise and Model Errors
Magnus Jansson, Björn Ottersten, Mats Viberg, Ali Fakoorian andA. Lee Swindlehurst · 2006
Signal parameter estimation and specically direction of arrival (DOA) es-timation for sensor array data is encountered in a number of applications ranging from electronic surveillance to wireless communications. Subspace based methods have shown to provide computationally as well as statis-tically ecient algorithms for DOA estimation. Estimator performance is ultimately limited by model disturbances such as measurement noise and model errors. Herein, we review a recently proposed framework that allows the derivation of optimal subspace methods taking both nite sample eects (noise) and model perturbations into account. We show how this general estimator reduces to well known techniques for cases when one disturbance dominates completely over the other. 1.1