2D-DOA Estimation for Coherent Signals via a Polarized Uniform Rectangular Array

This letter aims to estimate the 2-D direction-of-arrival (DOA) using a polarized uniform rectangular array (URA) under multipath propagation. To leverage the tensorial nature, a parallel factor (PARAFAC) model is established, in which it comprises two spatial response matrices, the polarization response matrix, and the source matrix. Unfortunately, the source matrix exhibits rank-deficiency, hindering effectively PARAFAC decomposition. Our analysis reveals that the rank-deficiency can be easily resolved by taking the KhatriRao product with a full column rank factor matrix. Consequently, three rearranged PARAFAC tensors are obtained that are free of the source matrix's rank-deficiency. The estimation of 2D-DOA is then performed using the vector cross product-auxiliary rotational invariance technique (VCPARIT). The proposed algorithms are insensitive to inter-sensor distance and are suitable for a one-snapshot scenario. Furthermore, they outperform existing smoothing methods from the perspective of estimation accuracy. Theoretical advantages of the proposed algorithms are corroborated by the simulations.

Automated summary

This letter proposes a method to estimate the 2-D direction-of-arrival (DOA) using a polarized uniform rectangular array (URA) under multipath propagation. The method establishes a parallel factor (PARAFAC) model that incorporates spatial response matrices, a polarization response matrix, and a source matrix. By taking the KhatriRao product with a full column rank factor matrix, the rank-deficiency of the source matrix is resolved, and three rearranged PARAFAC tensors are obtained. The estimation of 2D-DOA is then performed using the vector cross product-auxiliary rotational invariance technique (VCPARIT), which shows superiority over existing smoothing methods in terms of estimation accuracy.