In the practical radar with multiple antennas, the antenna imperfections degrade the system performance. In this paper, the problem of estimating the direction of arrival (DOA) in multiple-input and multiple-output (MIMO) radar system with unknown mutual coupling effect between antennas is investigated. To exploit the target sparsity in the spatial domain, the compressed sensing (CS)-based methods have been proposed by discretizing the detection area and formulating the dictionary matrix, so an \emph{off-grid} gap is caused by the discretization processes. In this paper, different from the present DOA estimation methods, both the off-grid gap due to the sparse sampling and the unknown mutual coupling effect between antennas are considered at the same time, and a novel sparse system model for DOA estimation is formulated. Then, a novel sparse Bayesian learning (SBL)-based method named sparse Bayesian learning with the mutual coupling (SBLMC) is proposed, where an expectation-maximum (EM)-based method is established to estimate all the unknown parameters including the noise variance, the mutual coupling vectors, the off-grid vector and the variance vector of scattering coefficients. Additionally, the prior distributions for all the unknown parameters are theoretically derived. With regard to the DOA estimation performance, the proposed SBLMC method can outperform state-of-the-art methods in the MIMO radar with unknown mutual coupling effect, while keeping the acceptable computational complexity.

The problem of direction of arrival (DOA) estimation has been studied for decades as an essential technology in enabling radar, wireless communications, and array signal processing related applications. In this paper, the DOA estimation problem in the scenario with gain-phase errors is considered, and a sparse model is formulated by exploiting the signal sparsity in the spatial domain. By proposing a new atomic norm, named as GP-ANM, an optimization method is formulated via deriving a dual norm of GP-ANM. Then, the corresponding semidefinite program (SDP) is given to estimate the DOA efficiently, where the SDP is obtained based on the Schur complement. Moreover, a regularization parameter is obtained theoretically in the convex optimization problem. Simulation results show that the proposed method outperforms the existing methods, including the subspace-based and sparse-based methods in the scenario with gain-phase errors.

A broadband dual-polarized notched-band antenna is proposed for the application of 2/3/4/5G base stations. The radiator of the antenna is composed of a pair of crossed printed dipoles, and is fed by both broadband baluns. Based on the broadband dual-polarized antenna with a tooth-shaped slot line in the baluns, a crossed dumbbell-shaped parasitic element is proposed above the radiator to generate a notch band from 2.9 to 3.1 GHz, while improve the impedance matching in the operating band (1.71 - 2.69 GHz & 3.4 - 3.6 GHz). To verify the principle, the antenna was fabricated and measured. The experimental results confirm that it achieves a bandwidth covering 1.71 - 3.6 GHz (VSWR < 1.5) with a notch band. The measured isolation is > 35 dB in the operating band. The antenna has a half-power beamwidth (HPBW) within 69.5 ± 4° and gain within 8.1 ± 0.4 dBi in the 2/3/4G bands, and a gain within 6.6 ± 0.5 dBi in the 5G band.

As a new type of super-low frequency (SLF, 30~300Hz) magnetic antenna, the permanent magnetic mechanical antenna (PMMA) has great potential for applications in underwater communication, underground communication, and earthquake prediction. PMMAs use large volume permanent magnets to enhance the signal, posing a significant challenge to power consumption of the mechanical drive structure. We optimize the permanent magnets in terms of magnetization structure, magnetic material, and form factor to determine the optimal permanent magnet structure. We establish an analytical model of the magnetic torque of the array elements and the radiated field of the array to determine the optimal array form. Based on the analytical model, we analyze the effects of the array element spacing, the phase difference of the array elements and the number of array elements on the radiated time-varying magnetic field. Finally, a 3D array prototype operating in frequency band of 75Hz~125Hz is developed for testing. The test results show that when the phase difference is less than 26°, the effect on the radiation intensity of the array is negligible. When the array element spacing is 0.15m, the motor cannot drive the mechanical structure, and when the array element spacing increases to 0.3m, it can operate normally. Communication experiments were conducted and the transmission rate could reach 2bps.

The computational complexity of the conventional adaptive beamformer is relatively large, and the performance degrades significantly due to both the model mismatch errors and the unwanted signals in received data. In this paper, an efficient unwanted signal removal and Gauss-Legendre quadra-ture (URGLQ)-based covariance matrix reconstruction method is proposed. Different from the prior covariance matrix recon-struction methods, a projection matrix is constructed to remove the unwanted signal from the received data, which improves the reconstruction accuracy of the covariance matrix. Considering that the computational complexity of most matrix reconstruction algorithms are relatively large due to the integral operation, we proposed a Gauss-Legendre quadrature-based method to approximate the integral operation while maintaining the accu-racy. Moreover, to improve the robustness of the beamformer, the mismatch in the desired steering vector is corrected by maximizing the output power of the beamformer under a constraint that the corrected steering vector cannot converge to any interference steering vector. Simulation results and prototype experiment demonstrate that the performance of the proposed beamformer outperforms the compared methods and is much closer to the optimal beamformer in different scenarios.