Near-Field 2D Radar Cross-Section Reconstruction Based on Modified TLS-ESPRIT Algorithm

A modified near-field total least-squares-estimating signal parameter via rotational invariance techniques (NF-TLS-ESPRIT) algorithm based on a two-dimensional (2-D) scattering-center (SC) model is proposed for accurate estimation of the positions and radar cross section (RCS) of point-scattering targets under limited-aperture near-field measurements. Conventional two-dimensional ESPRIT relies on a plane-wave observation model and requires the scattering-center number to be specified in advance, which may lead to position bias, model-order sensitivity, and unstable amplitude estimation when it is directly applied to finite-distance measurements. To address these problems, the proposed algorithm introduces a CLEAN-assisted dominant scattering-center number estimation procedure, a near-field coordinate compensation relation derived from spherical-wave geometry, and a multi-angle least-squares amplitude-estimation scheme for coherent far-field RCS reconstruction. Unlike conventional near-field to far-field transformation (NFFFT) approaches that focus mainly on field transformation, the proposed method also provides a corrected scattering-center representation for RCS reconstruction under limited angular apertures. Systematic simulations under different noise levels, angular apertures, relative bandwidths, model-order settings, scattering-center spacings, and dynamic ranges show that the proposed method improves scattering-center localization and RCS reconstruction accuracy in the tested limited-aperture cases. Measured multi-sphere experiments using directly measured reference RCS further verify the feasibility of the method for targets that can be represented by a finite number of dominant scattering centers.