Multiplicatively Regularized Source Reconstruction Method for Phaseless Planar Near-Field Antenna Measurements

Abstract

This paper approaches the source reconstruction method (SRM) for phaseless planar near-field (NF) antenna measurements by optimizing a multiplicatively regularized cost functional over the equivalent current distribution of the antenna under test (AUT). The utilized multiplicative regularization scheme, originally developed for image deblurring and inverse scattering problems, is adapted to phaseless NF antenna measurements performed over two parallel planes in front of the AUT. Current phaseless NF to far-field (FF) transformation techniques are highly dependent on the accuracy of the initial phase guess and the pattern features of the AUT. The proposed multiplicatively regularized SRM (MR-SRM) provides a robust and automated framework that can reconstruct the FF pattern of the AUT based on a nonsophisticated initial phase guess. In addition, advantages of the SRM are inherently incorporated into the MR-SRM, such as the capability for antenna diagnostics and allowing for extension to arbitrary measurement domains without the need for data interpolation. The cost functional associated with the MR-SRM is minimized with the conjugate gradient algorithm using closed-form expressions for gradient operators. The developed algorithm is presented in detail along with the synthetic and experimental examples demonstrating the method’s performance in different measurement scenarios along with a comparison to alternative methods.