Subarray is widely used in array antennas to decrease the system complexity, which would also increase the sidelobes during scanning. To solve this problem, subarray partition solutions with larger coverage and lower sidelobes are selected from a series of results generated by search algorithms. However, searching for an appropriate result of a large array is time-consuming. In this study, to search subarray solutions of full coverage with less time, we introduce two pruning processes in Algorithm X. Additionally, a pair of coefficients associated with the sidelobes is adopted for further pruning and reducing the sidelobes. Numerical experiments have been carried out and verified the effectiveness and the performance of the proposed method.

Sparse arrays have gained increasing research attention due to their potential to reduce system cost and weight. However, current studies on sparse array synthesis often overlook the physical dimensions of the antennas and only consider the distance constraints of antenna centers. In this communication, we propose a novel matrix constraints method for the sparse planar array synthesis, taking into account the actual area occupied by each antenna unit. The proposed method introduces a matrix that relates the aperture lattices to each candidate antenna; this matrix is then used as a spatial constraint for the antennas. The synthesis problem, aimed at achieving a low sidelobe level, is formulated as a mixed-integer optimization problem under this matrix constraint. To obtain the array layouts efficiently, the synthesis problem is relaxed to a compressed sensing problem and subsequently solved using a combination of re-weighted l ₁ minimization convex optimization and the integer genetic algorithm. Numerical experiments and full-wave simulations were conducted, verifying the effectiveness of the proposed method.

Calibration is important for digital monopulse radars since the target tracking performance would be degraded by the amplitude and phase imbalance among channels. However, the alignment between the radar to be calibrated and the test probe is a challenge, especially for antennas with high gain and narrow beamwidth. In this paper, we propose a fast alignment algorithm for the auto-calibration of digital monopulse radars using gradient ascent method based on the tentative radiation pattern gradient model. We verify the feasibility and the high- efficiency of the proposed algorithm by simulations and an experiment of a W-band digital monopulse radar. The elapsed time of the proposed algorithm is 0.01% of the existing method and requires lower equipment complexity compared with the scanning-based method.

This study investigates the electromagnetic scattering characteristics of lunar dust in the low-terahertz band. To address the scattering calculation challenges posed by complex lunar dust morphology, we propose a calculation method based on an equivalent ellipsoid model. Radar cross-section (RCS) simulations for single particles and particle ensembles validate the high consistency of the equivalent model. The calculated root mean square error (RMSE) is less than 0.11 dB and 2.06 dB, respectively. Based on these findings, we derive a terahertz radar equation specifically for lunar dust concentration retrieval. This study provides a theoretical foundation for the design of future lunar environmental detection systems and retrieval algorithms.

Polarization and micro-Doppler signatures are widely used for target detection and recognition in harsh sensing environments. However, accurately estimating polarization scattering matrices and Doppler frequencies of scattering centers remains challenging. This paper incorporates the multi-channel spectrum interpolation with the CLEAN algorithm. The proposed algorithm can estimate the Doppler frequencies and scattering matrices of target scattering centers from fully-polarized data with high precision. Compared with the traditional detection-after-imaging workflow, the proposed method takes an iterative suppression technique, which improves the estimation accuracy and reduces detection time. The effectiveness of the proposed method is verified based on the simulation and actual radar data. The estimation accuracy measured by polarized similarity is 99.9%, and the normalized root mean squared error of the estimated Doppler frequency is 4.8% in simulation.