Near-Field Localization With Exact Spherical Wavefront Based on L-Shaped COLD Arrays

Large-scale multiple-input multiple-output systems extend the Fresnel region, transforming the target source type from far-field (FF) to near-field (NF) with curved spherical wavefront, which poses challenges for accurate target localization. In this paper, an exact NF polarization localization algorithm is proposed to estimate the five-dimensional (5D) parameters by establishing an L-shaped co-centered orthogonal loop and dipole (COLD) array. First, estimation of manifold matrices aligned with each coordinate axis is performed by forming the delay cross-correlation covariance matrices, and employing parallel factor decomposition. The unambiguous phases present in each sub-manifold matrix are employed to disambiguate the remaining phases, enabling the construction of linear equations in spatial geometry, from which linear least squares parameter estimation is allowed. In particular, based on the rotational invariance relationship between the horizontal and vertical components received by the COLD array, the polarization parameters are estimated via total least squares. Under the exact NF polarization scenario, we derive the closed-form asymptotic parameter variances and the corresponding Cramér–Rao bound as benchmark. The proposed algorithm operates without phase approximation and is free from quarter-wavelength sensor spacing limitation, and extensive simulations are provided to demonstrate its superiority over competing schemes in terms of parameter estimation performance.