Recently, tensor network decomposition has attracted increasing attention due to its high efficacy in modeling multi-dimensional correlations in high-order tensors. Considering this advantage, this study employs tensor train decomposition (TTD) to achieve joint two-dimensional direction-of-departure (2D-DOD) and direction-of-arrival (2D-DOA) estimation in bistatic multiple-input multiple-output (MIMO) radar systems with uniform planar array (UPA) configurations. First, a five-dimensional (5D) tensor is defined to incorporate the two-dimensional (2D) spatial information from transmit and receive UPAs, which is then combined with temporal information. Next, a low-rank four-dimensional (4D) tensor is generated by performing an autocorrelation operation along the temporal dimension of the 5D tensor, forming a structure that encapsulates the difference coarrays of the transmit and receive UPAs. Then, a TTD framework is designed to decompose the 4D tensor into interconnected lower-order TT-cores. Furthermore, a precise bidirectional mapping is performed between the TT-cores and the original Vandermonde factor matrices. Finally, leveraging distinct combinations of TT-cores, two innovative methods are developed to recover the Vandermonde factor matrices. The proposed methods conduct automatic pairing of 2D-DOD and obtain 2D-DOA estimates by enforcing consistent permutation orders across all factor matrices. The proposed methods are verified by simulation experiments. The results confirm that the proposed methods can surpass the existing tensor-based methods in terms of estimation accuracy.

High cross-polarization isolation (CPI) is crucial to the accurate polarization measurement using polarimetric phased array radar (PPAR). In this paper, we propose an adaptive direction-dependent polarization state configuration (AD2PSC) method to improve the polarization isolation. Compared with conventional fixed polarization state of radiated wave whether it is linear, circular or elliptical polarization state, our AD2PSC approach configures the polarization state on basis of beam steering. To achieve the adaptive configuration of magnitude and phase of the dual-polarization antenna, an improved steepest descent algorithm is put forward. To facilitate the uniform representation for the polarization measurement application of PPAR, the universal expressions of intrinsic and measured backscatter matrices are derived for arbitrary polarization state. The dual-polarization dipole array is used to assess the priority of our proposed method. Compared with the conventional approaches, our approach could obtain higher CPI while being available for a larger scanning range. The configured CPI meets the specific polarization requirement for PPAR.

To reduce the cost of an n × n polarimetric phased array antenna, an innovative subarray tiling technology is proposed based on a geometrical property called rotational symmetry. This property requires the tiling solution to remain unchanged after a series of rotations and hence a consistent scanning performance in multiple beam directions is guaranteed. By considering the rotation as the reordering of the array elements, an improved Algorithm X is proposed, which is computationally efficient to find the rotational symmetric exact tiling solutions (RSETS). Due to a compressed scale of the RSETS and a faster dimension reduction of the dictionary matrix, it is convenient to obtain an RSETS with the scanning performance approximated that of the optimal solution. A 12 × 12 X-band dual-polarized microstrip patch array antenna is simulated for the verification of the proposed method under mutual coupling effects. A desired scanning performance is accomplished on both co-polarization and cross-polarization patterns of multiple beam directions, which is better than 99.75% of exact tiling solutions.

Conventional CANDECOMP/PARAFAC decomposition (CPD) and Tucker decomposition (TD) have been extensively used in bistatic multiple-input multiple-output (MIMO) radars for angle estimation. However, their specific decomposition methods can cause the loss of crucial information within a high-dimensional tensor, which requires improvements in estimation accuracy. To address this limitation, this study proposes an algorithm based on tensor train decomposition (TTD) to jointly estimate the two-dimensional direction-of-departure (2D-DOD) and two-dimensional direction-of-arrival (2D-DOA) values in bistatic MIMO radars with a uniform planar array geometry. First, a self-correlation tensor model is developed and reshaped into a low-rank four-dimensional (4D) coarray tensor with Vandermonde factors. Then, the tensor-train singular value decomposition (TT-SVD) method is employed to decompose this 4D coarray tensor into a head matrix, a tail matrix, and two third-order TT-core tensors. Furthermore, to mitigate the propagation of estimation errors across different Vandermonde factor matrices, this study designs an innovative coupled CPD model by integrating all inverse lateral slices of the second third-order TT-core tensor. In addition, the iterative least squares algorithm is employed to realize the coupled CPD, yielding three change-of-basis matrices and two Vandermonde factor matrices with trivial ambiguities. Finally, accurate automatic coupling of the 2D-DOD and 2D-DOA estimations is achieved, confirming that the estimated Vandermonde factor matrices have identical column permutations. The simulation results demonstrate that the estimation accuracy of the proposed algorithm is higher than that of the existing tensor decomposition algorithms.

In this letter, a coarray Tensor Train (TT) decomposition method is proposed to locate the targets in bistatic Multiple-Input Multiple-Output (MIMO) radar with uniform planar array (UPA) geometry. Initially, a five-dimensional (5-D) tensor model is established to preserve the multi-dimensional structure in the receive part. Subsequently, a four-dimensional (4-D) low-rank tensor can be obtained by removing redundant elements in the difference coarray of the eight-dimensional (8-D) covariance tensor. The TT-SVD algorithm is then applied to convert this 4-D tensor into a sequential product of lower-order TT-cores. Compared to CANDECOMP/PARAFAC decomposition (CPD) and Tucker decomposition (TD), the advantages of TTD include flexible multi-way data representation and mitigation of the curse of dimensionality. Furthermore, by using the relationship between Vandermonde factors matrices and TT-cores, a new method by combining different TT-cores is devised to estimate the angle parameters. Simulation results confirm the superiority of the proposed TT-aided method over other tensor-based methods for bistatic MIMO radar.

To reduce the cost of the polarimetric phased array radar, a novel array architecture conducive to horizontal (H) and vertical (V) polarization is proposed. The H/V polarization ports of each element are synthesized into a new port, of which the polarization is determined by the fixed phase shifter connected to its V polarization port. Then, different polarized elements are arranged properly within the aperture by designing the fixed phase shift of each element. The number of RF electronics is almost halved while the radiation performance can be maintained above 90% for H/V polarization. In this paper, the pattern synthesis model of the proposed architecture is formulated. Based on it, the evaluation function of the element polarization configuration (EPC) is established and optimized. By adopting the optimal EPC results into a 16×16 and a 32×32 planar array, the polarization radiation stability of the proposed architecture is demonstrated under sidelobe and cross-polarization constraints. By applying different quantization levels and phase deviations to the fixed phase shifters, the robustness of the proposed architecture is verified.