Asymptotic performance for subspace bearing methods with separable nuisance parameters in presence of modeling errors

This paper provides an asymptotic (in the number of snapshots) closed form expression of the bias and RMS (root mean square) error of the estimated DOA (direction of arrival) for the algorithm recently introduced in Ferreol et al. (2004). This algorithm provides a 1D DOA estimation in a multi-parameter context where the DOA have to be estimated with some separable nuisance parameters. Results are based on a second order approximation of the criterion. DOA estimation errors are then expressed as a ratio of Hermitian forms of multivariate complex random variables. Theoretical results are validated by simulations in a self-calibration context.