Multi-functional antenna array has an ability to significantly simplify the antenna system in multi-functional radar or communication systems since it can work in multiple frequency bands simultaneously. To further reduce the system overhead, such as RF front-ends and digital processing channels, this paper discusses the joint design of antenna selection and receive beamforming for multi-functional antenna arrays. With the assistance of a RF switch network, K antennas selected from an N-element linear array (K < N) only require K RF frontends and digital processing channels. To maximize the output signal-to-interference-plus-noise ratio (SINR) and control the sidelobe levels concurrently, the proposed multi-functional sparse array design can be formulated into a nonconvex constrained optimization problem with a l2,0-mixed norm regularization. This problem ensures that the selected antenna positions are the same in all operating frequency bands while the beamforming weights of each frequency band are jointly optimized. In virtue of the consensus alternating direction method of multipliers (CADMM) and reweighted l2,1-norm approximation techniques, a CADMMbased iteratively reweighted algorithm is proposed to handle this problem effectively, where the original problem is converted into a series of subproblems whose optimal solutions are all readily obtained. The convergence and computational complexity are also analysed. Numerical results show that the proposed multifunctional sparse array remarkably reduces the sidelobe level in all operating frequency bands concurrently while maintains the maximum output SINR, and thus has superior performance on interference suppression.

Space‐time adaptive processing (STAP) with element‐pulse coding (EPC)‐multiple‐input multiple‐output (MIMO) radars is capable of suppressing range ambiguous clutter in airborne radar fields. However, clutter rank has not been investigated in EPC‐MIMO radars, which is of great significance for the design of STAP algorithms, such as reduced‐rank and reduced dimension, etc. This paper studies the clutter rank estimation thoroughly. Firstly, a subspace transformation matrix including the entire clutter subspace is constructed, which is related to the spacing ratio of transmit and receive elements, the number of Doppler ambiguity and the number of range ambiguity. Secondly, the clutter rank is accurately estimated according to the subspace transformation matrix for any element spacing ratio, number of Doppler ambiguity, and number of range ambiguity. Moreover, based on the estimated clutter rank, the maximum resolvable number of range ambiguity is derived. Ultimately, the impact of channel weighting on clutter rank is analysed. Numerical results verify the accuracy of the estimated clutter rank and maximum resolvable number of range ambiguity.

The one-bit cross-dipole array employs one-bit quantization to reduce the sampling system overhead while extracting polarization information from electromagnetic signals, thereby lowering the system complexity of the polarization array. However, the existing DOA estimation algorithms for this array utilize incomplete information from the received signals of the array. Cross-dipole array consists of two scalar arrays orthogonal to each other. With one-bit measurements, the array outputs two one-bit scalar array datum. By exploiting the two array datum, we can generate two covariance matrices and two cross-covariance matrices of the cross-dipole array signals. Existing direction-of-arrival (DOA) estimation algorithms based on one-bit measurements only realize DOA estimation by combining two covariance matrices, resulting in insufficient mining of information. In this paper,we propose a DOA estimation method for completely polarized signals that jointly utilizes auto-covariance and cross-covariance. To do this, we first study the reconstruction of two cross-covariance matrices from one-bit measurements. Based on the analyses of four covariance matrices, we then formulate virtual array datum consisting of four virtual snapshots by difference array technique, called parallel coarray signals. As the parallel coarray signals are coherent although the incoming sources are independent, we employ the spatial-smoothing technique on the parallel coarray signals to perform the DOA estimation. Numerical results show that the performance of the proposed method is significantly improved for completely polarized signals.

Sparse array arrangement has been widely used in vector-sensor arrays because of increased degree-of-freedoms for identifying more sources than sensors. For large-size sparse vector-sensor arrays, one-bit measurements can further reduce the receiver system complexity by using low-resolution ADCs. In this paper, we present a sparse cross-dipole array with one-bit measurements to estimate Direction of Arrivals (DOA) of electromagnetic sources. Based on the independence assumption of sources, we establish the relation between the covariance matrix of one-bit measurements and that of unquantized measurements by Bussgang Theorem. Then we develop a Spatial-Smooth MUSIC (SS-MUSIC) based method, One-Bit MUSIC (OB-MUSIC), to estimate the DOAs. By jointly utilizing the covariance matrices of two dipole arrays, we find that OB-MUSIC is robust against polarization states. We also derive the Cramer-Rao bound (CRB) of DOA estimation for the proposed scheme. Furthermore, we theoretically analyze the applicability of the independence assumption of sources, which is the fundamental of the proposed and other typical methods, and verify the assumption in typical communication applications. Numerical results show that, with the same number of sensors, one-bit sparse cross-dipole arrays have comparable performance with unquantized uniform linear arrays and thus provide a compromise between the DOA estimation performance and the system complexity.

Multi-source radar data fusion has become increasingly vital for advancing weather monitoring and forecasting. However, effectively integrating Doppler radar with an X-band phased-array radar remains challenging. Doppler radar offers only low and inconsistent spatial resolution, whereas an X-band phased-array radar provides high resolution but is limited by short detection range, severe signal attenuation, and high deployment costs, constraining its use to localized monitoring. To address the aforementioned challenges, this paper proposes the Multi-source Radar Reflectivity Complementary Enhancement method (MSR-CE). By constructing a paired training dataset, real X-band phased-array radar reflectivity data serve as the starting samples for the forward diffusion process, while paired S-band Doppler radar reflectivity data act as conditional guidance. Leveraging a conditional diffusion model, the method generates high-resolution pseudo X-band phased-array reflectivity fields. Additionally, a Radar-Physics-Aware Loss (RPA Loss) is introduced to enhance spatial detail fidelity and physical consistency. Experiments on multi-source radar observations from Northeast China in 2025 demonstrate that MSR-CE achieves an SSIM of 0.892 and a PSNR of 41.6 dB, outperforming traditional interpolation methods and state-of-the-art generative approaches in radar reflectivity enhancement.