A PERFORMANCE ANALYSIS OF SUBSPACE-BASED METHODS IN THE PRESENCE OF MODEL ERRORS - .2. MULTIDIMENSIONAL ALGORITHMS

This is the second of a two-part paper dealing with the performance of subspace-based algorithms for narrow-band direction-of-arrival (DOA) estimation when the array manifold and noise covariance are not correc tly modeled. In Part I, the performance of the MUSIC algorithm was inv estigated. In Part II, we extend this analysis to multidimensional (MD ) subspace-based algorithms including deterministic (or conditional) m aximum likelihood, MD-MUSIC, weighted subspace fitting (WSF), MODE, an d ESPRIT. A general expression for the variance of the DOA estimates i s presented that can be applied to any of the above algorithms and to any of a wide variety of scenarios (e.g., gain/phase errors, mutual co upling, sensor position errors, noise covariance mismodeling, etc.). O ptimally weighted subspace fitting algorithms are also presented for s pecial cases involving random unstructured errors to the array manifol d and noise covariance. In addition, it is shown that one-dimensional MUSIC outperforms all of the above MD algorithms for random angle-inde pendent array perturbations.