Xin Lai

In this paper, we consider the bistatic coprime MIMO radar in which the receive array is assembled on a movable platform and research the issue of jointly estimating the direction of departure (DOD) and direction of arrival (DOA). Specifically, we propose a dilated coprime array with nested motions (DCANM) that exploits the nested motions of the dilated coprime array to enlarge the virtual array in view of the synthetic sum-difference coarray (SDCA) concept. However, the holes in the synthetic SDCA of the bistatic coprime MIMO radar cause the performance loss of subspace-based coarray methods. To address this problem, we interpolate the holes of synthetic SDCA and propose a spatially smoothed interpolated coarray completion (SSICC) method to solve the rank deduction problem caused by the whole missing slices of SDCA, which helps to enhance the uniform DOFs and identifiability. Finally, extensive simulation results are carried out to demonstrate the superiorities of the proposed methods in estimation accuracy and resolution.

In this paper, we consider monostatic sparse MIMO radar with the transmit and receive arrays mounted on a moving platform and exploit array motions to increase the number of degrees of freedom (DOFs) and continuous lags for direction of arrival (DOA) estimation. A synthetic sum coarray of difference coarray (SSCDC) concept is proposed to interpret the virtual arrays collected from the sparse MIMO radar with multiple array positions, followed by a dilated sparse MIMO radar family by enlarging the transmit and receive array geometries of the existing sparse MIMO radar. Properties of the dilated sparse MIMO radar in terms of the uniform SSCDC and the number of continuous lags related to array motions are derived. To further increase the DOFs, a uniform sampling moving (USM) strategy is introduced and then a nested sampling moving (NSM) strategy is designed by utilizing the sparsity of array motion. Furthermore, theoretical performance of the moving sparse MIMO radar is evaluated via the synthetic coarray Cramer-Rao bound (CRB). Closed-form expressions for uniform DOFs for the dilated sparse MIMO radar with the proposed moving array strategies are derived and numerical simulations are provided to demonstrate their performance.

This paper presents a new technique for estimating the two-dimensional direction of departure (2D-DOD) and direction of arrival (2D-DOA) in bistatic uniform planar array Multiple-Input Multiple-Output (MIMO) radar systems. The method is based on the reduced-dimension (RD) MUSIC algorithm, aiming to achieve improved precision and computational efficiency. Primarily, this pioneering approach efficiently transforms the four-dimensional (4D) estimation problem into two-dimensional (2D) searches, thus reducing the computational complexity typically associated with conventional MUSIC algorithms. Then, exploits the spatial diversity of array response vectors to construct a 4D spatial spectrum function, which is crucial in resolving the complex angular parameters of multiple simultaneous targets. Finally, the objective is to simplify the spatial spectrum to a 2D search within a 4D measurement space to achieve an optimal balance between efficiency and accuracy. Simulation results validate the effectiveness of our proposed algorithm compared to several existing approaches, demonstrating its robustness in accurately estimating 2D-DOD and 2D-DOA across various scenarios. The proposed technique shows significant computational savings and high-resolution estimations and maintains high precision, setting a new benchmark for future explorations in the field.

Coprime arrays have shown desirable properties in reducing mutual coupling and increasing degrees of freedom (DOF) compared to uniform linear arrays (ULAs), which helps improve the estimation performance. However, the holes in the difference coarray of coprime arrays cause estimation performance loss. In this paper, we present a multi-layer holes filling strategy for coprime arrays to fill the holes, thus enhancing the uniform difference coarray. Specifically, we first construct a coprime array with multiple duplicated subarrays (CAMDS) and classify the holes in the difference coarray into multiple layers. Then we present a multi-layer filled coprime array (MLFCA) by introducing extra subarrays to fill the holes in different layers, which resultantly achieves significantly increased uniform DOFs (uDOFs). We then derive the optimal MLFCA in the scale of difference coarray and develop an optimized structure to extend the difference coarray. Finally, the proposed coprime arrays enjoy a hole-free difference coarray and achieve a larger uniform difference coarray than previous coprime arrays. Simulation results have verified the merits of the proposed coprime arrays through the subspace-based estimator.

Traditional polarization-sensitive sensors involve a triplet of spatially collocated, orthogonally oriented, and diversely polarized electric dipoles. However, this kind of sensor has the drawback of severe mutual coupling among the three dipoles due to the characteristic of collocation, as well as low radiation efficiency because of the short length of the dipoles. Based on this problem, in this study we designed a new array structure called a ‘triple coprime array (TCA)’, equipped with long electric dipoles to obtain higher radiation efficiency. In this structure, the dipoles within different subarrays have orthogonal polarization modes, leading to mutual coupling isolation. The dipole interval of the subarrays is enlarged by means of a pairwise coprime relationship, which further weakens the mutual coupling effect and extends the array aperture. Simultaneously, a stable direction-of-arrival (DOA) and polarization estimation method is proposed. DOA information is accurately refined from the three subarrays without ambiguity problems, with the triple coprime characteristic improving the estimation results. Subsequently, polarization estimates can be obtained using the reconstructed model matrix and the least squares method. Numerous theoretical analyses were conducted and extensive simulation results verified the advantages of the TCA structure in mutual coupling, along with the superiority of the proposed joint DOA and polarization estimation algorithm in terms of estimation accuracy.

To address the problem of expensive computation in traditional two-dimensional (2D) direction of arrival (DOA) estimation, in this paper, we propose a 2D DOA estimation method based on a reduced dimension and root-finding MUSIC algorithm for nested planar arrays (NPAs). Specifically, the algorithm proposed in this paper transforms the problem based on 2D spectral peak search into two one-dimensional estimation problems by reducing the dimension, and then transforms the one-dimensional estimation problem into a problem of polynomial root finding. Finally the parameters are paired to realize the 2D DOA estimation. The proposed algorithm not only performs two root finding operations directly according to the 2D spectral function transformation, avoiding the performance degradation caused by intermediate operations, but can also fully exploit the enlarged array aperture offered by NPAs with reduced computational complexity and no need for virtualization. The superiorities of the proposed algorithm in terms of estimation accuracy and complexity are verified by simulations.