A Performance Analysis ofSubspace - Based Methods in thePresence of Model Errors { Part II : Multidimensional

This is the second of a two-part paper dealing with the performance of subspace-based algorithms for narrowband direction-of-arrival (DOA) estimation when the array manifold and noise covariance are not correctly modeled. In Part I, the performance of the MUSIC algorithm was investigated. In Part II, we extend this analysis to mul-tidimensional (MD) subspace-based algorithms including deterministic (or conditional) maximum likelihood, MD-MUSIC, weighted subspace tting (WSF), MODE, and ES-PRIT. A general expression for the variance of the DOA estimates is 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 coupling, sensor position errors, noise covariance mismodel-ing, etc.). Optimally weighted subspace tting algorithms are also presented for special cases involving random unstructured errors to the array manifold and noise covariance. In addition, it is shown that one-dimensional MUSIC outperforms all of the above MD algorithms for random angle-independent array perturbations.