Array calibration by Fourier series parameterization: scaled principal components method

Based on a Fourier series model for an antenna array´s response and a typical calibration procedure, a maximum likelihood (ML) solution for the array parameters can be derived. The authors review the Fourier series model of an array´s response and introduce a suboptimum method of determining the model parameters. The performance of the suboptimal solution is significantly influenced by the scaling of the principal components (M.A. Koerber, 1992). A method of scaling based on a QR decomposition is presented. This method provides for approximately an order of magnitude reduction in error over previously reported scaling methods. This approach has the significant advantage of requiring no a priori knowledge of the array´s response. Simulation results compare the use of QR decomposition based scaling with the ML solution.<>