DOA Estimation-Based Localization Algorithm for Polarization-Assisted UAV-Borne Radar Systems

Unmanned aerial vehicle (UAV)-borne radar systems have emerged as a core technology for wide-area, high-efficiency target localization and monitoring. However, traditional UAV-borne radar systems are typically configured with uniform linear scalar sensor arrays, in which mutual coupling effects and limited aperture signiffcantly limit the positioning performance. In this paper, a polarization-assisted UAV-borne radar localization system is developed. This system comprises UAVs outfitted with coprime vector sensor arrays. Furthermore, a tensor-based direction-of-arrival (DOA) estimation algorithm leveraging atomic norm minimization (ANM-Tensor-DOA) and a polarization-assisted cross-localization (PACL) technique are introduced. Specifically, an ANM-based optimization task is established based on the cross-correlation matrices of the polarization components to reconstruct the Hermitian Toeplitz-structured noiseless information matrix and the measurement matrix. Subsequently, an augmented noiseless tensor model is established, allowing DOA to be estimated via tensor decomposition. Then the polarization states are determined via closed-form expressions derived from the measurement matrix. Ultimately, based on the known DOA and polarization information, the target’s location can be determined using the proposed PACL algorithm. Simulation results indicate that, compared to more recent methods, the proposed approach delivers improved parameter estimation performance and high-precision localization capabilities.