Enhanced 2D DOA and polarization estimation for sparse distributed orthogonal loop and dipole planar array based on fourth-order cumulant

Two-dimensional (2D) direction-of-arrival (DOA) and polarization estimation using sparse polarimetric array shows advantages in increasing degrees-of-freedom (DoFs) and reducing hardware costs. However, most relevant studies still rely on second-order statistics, which constrain the achievable DoFs. To overcome such limitations, we propose a fourth-order cumulant-based approach for multi-parameter estimation in joint spatial-polarimetric domains. Via such an approach, a covariance-like standard cumulant matrix corresponding to a virtual uniform counterpart of the considered sparse distributed orthogonal loop and dipole planar array is constructed, where we have defined the involved selection matrices in the data reordering process. A virtual spatial-polarimetric rotational-invariance procedure is then presented to obtain an efficient estimation of 2D DOA and polarization in closed form. Simulation results are then included to verify the performance advantages of the proposed approach in terms of identifiability, estimation accuracy, probability of successful resolution, and computational efficiency.