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.

Intelligent reflecting surfaces (IRS) enable radar sensing in blocked environments by reconfiguring propagation and creating virtual apertures, which is crucial for non-line-of-sight (NLoS) localization. This work addresses high-accuracy direction-of-arrival (DoA) estimation in semi-passive IRS-assisted sensing. We introduce a virtual-domain lifting that vectorizes the received echoes and induces a structured atomic set, leading to an atomic norm minimization (ANM) formulation. The ANM estimator is formulated as a semidefinite program (SDP) via convex relaxation, and we develop an accelerated proximal gradient (APG) solver that leverages the problem structure and avoids interior-point steps, resulting in substantial computational savings. Compared with spatial-domain and grid-based approaches, the transformed-domain estimator delivers an optimal accuracy-complexity tradeoff. It achieves gridless (ANM-level) high resolution while reducing runtime. Extensive simulations across array sizes, transmit power, and IRS configurations confirm accuracy, robustness to off-grid mismatch, and scalability, demonstrating the practicality of the proposed method for IRS-enabled NLoS sensing in complex environments.

In this letter, we develop a coarray interpolation method for direction-of-arrival (DOA) estimation using the coprime electromagnetic vector-sensor (EMVS) array. Firstly, we derive the coarray signal model of the coprime EMVS array, which can be viewed as a combination of multiple polarization components. Subsequently, we fill in zero elements in each polarization component and recover the corresponding low-rank covariance matrix by solving a nuclear norm minimization (NNM) problem. By exploiting the recovered covariance matrices, we eventually construct a larger covariance matrix to carry out DOA estimation. Numerical experiment results demonstrate the superiority of the proposed algorithm over conventional methods.