In this paper, we propose a higher-order unitary propagator method (HO-UPM) to estimate two dimensional (2D) direction-of-arrival (DOA) of Non-Circular (NC) sources via a uniform rectangular array (URA) of antennas. The proposed method benefits from the jointly augmented multidimensional structure of measurement data and enhances the estimation accuracy by exploiting the NC property with an additional advantage of inexpensive computations as compared to the higher-order singular value decomposition (HOSVD). Though the joint mode augmentation brings redundancy in the resulting NC manifold tensor, it still doubles the antenna count virtually and yields a larger URA that acts as a 2D separable spatial sampling grid retaining the centro-symmetry and translational invariance. Therefore, the proposed approach not only improves the model identifiability but also avoids individual augmentation and processing of each mode of the measurement tensor. Numerical simulations displaying accuracy of subspace estimation, computational cost and root mean square error (RMSE) performance along-with the deterministic NC Cramer-Rao bound (CRB) are also included to verify and compare the effectiveness of proposed method with the existing matrix and tensor-based approaches.

在本文中，我们提出了一种高阶unit传播器方法（HO-UPM），通过均匀的矩形阵列（URA）估计非圆形（NC）源的二维（2D）到达方向（DOA）天线。与高阶奇异值分解（HOSVD）相比，该方法受益于测量数据的联合增强的多维结构，并通过利用NC属性提高了估算精度，并具有廉价计算的额外优势。尽管联合模式增强在最终的NC流形张量中带来了冗余，但实际上它仍然使天线数量增加了一倍，并产生了更大的URA，可作为2D可分离的空间采样网格，保留了中心对称性和平移不变性。因此，所提出的方法不仅提高了模型的可识别性，而且避免了对测量张量的每种模式的单独扩充和处理。数值模拟还显示了子空间估计的准确性，计算成本和均方根误差（RMSE）性能，以及确定性NC Cramer-Rao界（CRB），以验证和比较该方法与现有矩阵和张量的有效性基于方法。