Synthesizing Unequally Spaced Pattern-Reconfigurable Linear Arrays With Minimum Interspacing Control
Synthesizing Unequally Spaced Pattern-Reconfigurable Linear Arrays With Minimum Interspacing Control
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DOI：
10.1109/access.2019.2914767
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发表时间：
2019-05
期刊：
影响因子：
3.9
通讯作者：
Yuqi Yang;Yanhui Liu;Xinyu Ma;Ming Li;K. Xu;Y. Guo
中科院分区：
文献类型：
--
作者：
Yuqi Yang;Yanhui Liu;Xinyu Ma;Ming Li;K. Xu;Y. Guo
以前，交替凸优化（ACO）被用来减少在单一模式的线阵列中的元素的数量。本文将蚁群算法推广到具有可重构多方向图的不等间距稀疏线阵的综合。在这种扩展的蚁群算法中，最小间距约束可以很容易地纳入稀疏阵列综合通过执行一组约束交替凸优化。通过三个不同方向图要求的稀疏线阵综合算例验证了该方法的有效性、鲁棒性和优越性。综合结果表明，该方法能有效地减少可重构多方向图线阵的阵元数，并能很好地控制旁瓣电平和最小间距。并与其它方法进行了比较。
Previously, the alternating convex optimization (ACO) was used to reduce the number of elements in the single-pattern linear array. This work extends the ACO method to synthesize the unequally spaced sparse linear arrays with reconfigurable multiple patterns. In this extended ACO, the minimum interspacing constraint can be easily incorporated in the sparse array synthesis by performing a set of constrained alternating convex optimizations. Three examples for synthesizing sparse linear array with different multiple-pattern requirements are conducted to validate the effectiveness, robustness, and advantages of the proposed method. The synthesis results show that the proposed method can effectively reduce the number of elements in the reconfigurable multiple-pattern linear arrays with good control of the sidelobe levels and minimum interspacing. The comparisons with other methods are also given in the examples.