Sparse linear arrays (SLAs) can be designed in a systematic way, with the ability for underdetermined DOA estimation where a greater number of sources can be detected than that of sensors. In this paper, as the first stage, a new systematic design named multi-subarray dilated nested array (MDNA), whose difference co-array (DCA) can be proved to be hole-free, is firstly proposed by introducing a sparse ULA and multiple identical dense ULAs with appropriate sub-ULA spacings. The MDNA will degenerate into the nested array under specific conditions, and the uniform degrees of freedom (uDOFs) of MDNA is larger than that of its parent nested array. On the basis of MDNA, to reduce the mutual coupling effect, an augmented multi-subarray dilated nested array (AMDNA) is constructed by migrating some elements of the dense segments of MDNA, without reducing the number of uDOFs. Several theoretical properties of the proposed array structures are proved, and simulation results are provided to demonstrate the effectiveness and superiority of the proposed AMDNA over some existing sparse arrays.

In this paper, a novel localization method for near-field (NF) rectilinear or strictly noncircular sources with a symmetric uniform linear array of rgb0,0,0cocentered orthogonal loop and dipole (COLD) antennas is proposed. Based on the rank reduction (RARE) principle, the multiple parameters including direction of arrival (DOA), range and polarization parameters are separated. Furthermore, a closed-form solution for polarization parameters and noncircular phases is also provided. The deterministic Cramer-Rao bound (CRB) of the estimation problem under consideration is also derived as a benchmark. Numerical simulations are provided to demonstrate the effectiveness of the proposed method.

Unknown mutual coupling effect can degrade the performance of a direction of arrival estimation method. In this letter, a new method is proposed for uniform linear arrays (ULAs) to tackle this problem. Considering the sparse representation exploiting the inherent structure of the received data, the effective block sparse representation and the convex optimization problem are constructed using the steering vector parameterizing method. The proposed solution based on the ${l} _{1}$ - singular value decomposition can exploit the information provided by the whole array and the Toeplitz structure of the mutual coupling matrix in the ULA. Simulation results are provided to demonstrate its performance with unknown mutual coupling in comparison with some existing methods.

In this article, a novel vehicle positioning system architecture based on direction-of-arrival (DOA) estimation of incoherently distributed (ID) sources is proposed employing massive multiple-input–multiple-output (MIMO) arrays. Such an architecture with the associated signal model is more consistent with the actual array application and multipath transmission scenarios. First, an end-to-end two-dimensional (2-D) DOA estimation of ID sources utilizing a dual one-dimensional (1-D) convolutional neural network (D1D-CNN) under the deep learning (DL) framework is performed, where the normalized covariance matrix data is used for both offline training and online estimation. Then, the received SNR information is exploited to select a set of DOA estimates provided by multiple collaborative BSs for positioning. Moreover, transfer learning and an attention mechanism are employed to promote its generalization ability and achieve robustness against array perturbations. Simulation results are provided to show that the proposed method outperforms the state-of-the-art methods in terms of computational complexity, positioning accuracy, and robustness against array perturbations.

A three-dimensional (3-D) near-field (NF) source localization method is introduced for a bistatic multiple-input multiple-output (MIMO) radar system equipped with arbitrary electromagnetic vector sensors (EMVSs) at both the transmitter and receiver. Firstly, to obtain estimates of the steering matrices of the transmitting array and the receiving array, tensor decomposition is performed on the covariance matrix of the outputs. Then, multiple parameters of targets, including two-dimensional direction-of-departure (2D-DOD), range from transmitter to target (RFTT), along with two-dimensional direction-of-arrival (2D-DOA), range from target to receiver (RFTR), and two-dimensional receive polarization angle (2D-RPA) with respect to receiver can be obtained by exploiting the property of rotation invariance. Subsequently, a linear equation can be constructed to determine all location parameters of NF targets. The proposed method can provide automatically paired multi-parameters without peak search and has low computational complexity. Moreover, the Cramer-Rao bound (CRB) is derived as a performance benchmark for the model under consideration, and the effectiveness of the proposed method is illustrated by a series of numerical simulations.