Robust Calibration Algorithm for Multiplicative Modeling Errors Against Location Deviations of Auxiliary Sources.

It is well known that the azimuth deviations of the auxiliary sources severely degrade the performance of classical subspace-based calibration methods that assume the direction-of-arrivals of calibration sources are perfectly measured. Therefore, aiming at the effects of source location deviations, the estimation variance of the multiplicative modeling errors for the subspace-based calibration method is first derived by applying matrix eigen-perturbation theory and first-order perturbation analysis approach. The theoretical analysis is undertaken under the assumption that the azimuth deviations are small enough for the first-order perturbation analysis to be valid. In addition, to mitigate the effects of the location errors, a structured total least squares optimization model is established using first-order Taylor series expansion method. Then, the corresponding numerical algorithm is presented to provide a robust estimate for multiplicative modeling errors. The exact Cramér-Rao bound expressions for the unknowns are also deduced in the presence of the azimuth deviations. Simulation results confirm the effectiveness of the theoretical analysis and demonstrate the desirable behavior of the robust calibration algorithm in comparison with the subspace-based calibration methods. [ABSTRACT FROM AUTHOR]