Sampling Reduction for Near-Field to Far-Field Transformation of Bistatic Radar Target Measurements

Abstract

Accurate bistatic radar cross-section (RCS) characterization requires near-field measurements over multiple dimensions, which, under typical near-field sampling requirements, can lead to long measurement durations for electrically large radar targets. This paper presents a near-field-to-far-field transformation based on an equivalent surface-current reconstruction using a-priori information about the target to reduce the number of newly acquired near-field samples. It is assumed that the prior target and the target under test (TUT) differ by localized geometrical modifications. Their complex differential near field is reconstructed using Tikhonov-regularized least squares QR factorization (LSQR) method. The reconstructed differential far- field contribution is coherently combined with the prior far-field contribution to estimate the bi-static RCS of the TUT. The method is preliminarily evaluated using simulations of a complex target at 6 GHz. The results indicate that, for the investigated simulation case, prior information can be used to thin out the sampling grid by 90% while maintaining close agreement with the simulation baseline.