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Array Geometry Optimization With Excitation Quantization Constraints: A Mixed-Integer Programming Approach
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DOI：10.1016/j.dsp.2025.105795.png)
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Abstract
En 中文
This paper addresses the problem of array geometry optimization, with a specific focus on array layout designs that incorporate phase and/or amplitude quantization constraints applied to the excitation. By jointly optimizing the antenna selection states and position offsets, we introduce a continuous antenna selection scheme and develop a framework for array geometry optimization using mixed integer programming (MIP). Our method is applicable to both one-dimensional linear arrays and two-dimensional planar arrays, and accommodates a wide range of pattern synthesis scenarios with excitation quantization constraints. Moreover, the proposed method supports multi-objective optimization for array geometry design. In this paper, we investigate linearization and equivalent reformulation techniques across different array geometry optimization scenarios, thereby obtaining tractable MIP models that can be effectively solved using off-the-shelf solvers. Theoretical analysis is provided to support the rationality of our approach. Extensive simulations are conducted to demonstrate the effectiveness and superiority of the proposed method, validating its performance under various scenarios.
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D
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3
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653
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