Signal parameter estimators which are less sensitive to perturbations in the array manifold are presented. A parametrized stochastic model for the array uncertainties is introduced. The unknown array parameters can include the individual gain and phase responses of the sensors as well as their positions. Based on this model, a maximum a posteriori (MAP) estimator is formulated. This results in a fairly complex optimization problem which is computationally expensive. The MAP estimator is simplified by exploiting properties of the weighted subspace fitting method. An approximate method that further reduces the complexity is also presented, assuming small array perturbations. A compact expression for the MAP Cramer-Rao bound (CRB) on the signal and array parameter estimates is derived. A simulation study indicates that the proposed robust estimation procedures achieve the MAP-CRB even for moderate sample sizes.