Optimal Subspace Techniques for DOA Estimation in the Presence of Noise and Model Errors

Signal parameter estimation and specifically direction of arrival (DOA) estimation 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 statistically efficient 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 finite sample effects (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.