研究目的
To optimize the subarray configuration and digital weights for large phased arrays to efficiently handle tasks such as synthesizing multiple beams and adaptively suppressing interferences, using an alternating minimization approach to reduce computational complexity.
研究成果
The proposed alternating minimization approach effectively optimizes subarray configuration and digital weights for large phased arrays, demonstrating improved performance in beam synthesis and interference suppression compared to existing methods, with low computational cost suitable for practical implementations.
研究不足
The approach assumes a digital phased array with 0/1 operations for subarray formation, which may have hardware constraints. The optimization is NP-hard and relies on approximations; performance may degrade with very large arrays or complex interference environments. The simulations are numerical and may not fully capture real-world conditions.
1：Experimental Design and Method Selection:
The study uses mathematical formulations for static beam pattern matching and adaptive interference suppression, employing an alternating minimization algorithm to solve the mixed 0-1 integer programs efficiently.
2：Sample Selection and Data Sources:
Numerical simulations are conducted on an elliptic planar array with 440 elements, with specific parameters such as element spacing at half wavelength and 20 subarrays.
3：List of Experimental Equipment and Materials:
Not explicitly mentioned in the paper.
4：Experimental Procedures and Operational Workflow:
The algorithm iteratively updates subarray weights and configurations, with simulations comparing the proposed approach to uniform and quantization methods under various interference scenarios.
5：Data Analysis Methods:
Performance is evaluated based on beam pattern matching, output SINR, and computational efficiency, using metrics like side lobe levels and pattern distortion.
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