Asymptotic performance of optimal gain-and-phase estimators of sensor arrays

For estimating angles of arrival, there are three well known algorithms: we ighted noise subspace fitting (WNSF), unconditional maximum likelihood (UML ), and conditional niaximum likelihood (CML). These algorithms can also be used for estimating/calibrating the gains-and-phases of sensor arrays, assu ming the angles of arrival are known. We show that the WNSF algorithm with an optimal weight has the same statistical efficiency as the UML algorithm but more efficient than the CML algorithm. This conclusion was known for an gles df arrival estimation and is now confirmed for gains-and-phases calibr ation. Computationally, the WNSF algorithm is shown to be more attractive t han the other two as it can be implemented via a quadratic minimization pro cedure for arbitrarily shaped arrays.