研究目的
To address the challenge of achieving full coverage and good radiation performance in subarrayed phased arrays by formulating the subarray design as an iterative convex relaxation optimization problem and evaluating its performance in adaptive beamforming.
研究成果
The proposed iterative convex relaxation method effectively achieves full coverage solutions for modular subarrayed phased arrays with good scanning radiation performance. It shows inherent superiority in adaptive beamforming by maintaining equal output noise energy across subarrays, avoiding the need for normalization pre-processing. The method is expandable to arbitrary array apertures and subarray structures, with future work aimed at joint optimization of coverage and performance.
研究不足
The method may be computationally intensive for very large-scale arrays, and the optimization does not jointly consider full coverage and radiation performance in a single step, requiring separate evaluation of SSL. It is primarily validated for specific subarray shapes and array apertures.
1：Experimental Design and Method Selection:
The subarray design problem is formulated as a binary integer optimization problem and solved using iterative convex relaxation based on difference of convex sets theory. Linear programming in MATLAB is used for iterations.
2：Sample Selection and Data Sources:
A rectangular aperture planar array with 432 elements (M=36, N=12) and L-octomino-shaped subarrays are used. The element pattern is assumed to be cos^(1/2)θ.
3：List of Experimental Equipment and Materials:
A
4：9-GHz PC for running simulations, MATLAB software for computations. Experimental Procedures and Operational Workflow:
Generate binary matrix L for the array aperture and subarray candidates, perform iterative convex relaxation to find full coverage solutions, evaluate scanning sidelobe level (SSL) to select optimal configuration, and analyze adaptive beamforming properties.
5：Data Analysis Methods:
Convergence is assessed using residual values, radiation patterns are plotted, and comparisons are made with other methods like GA, BCS, ETM, and Algorithm X.
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