2) uncorrelat'/> Optimizing Minimum Redundancy Arrays for Robustness
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Optimizing Minimum Redundancy Arrays for Robustness

机译：优化鲁棒性的最小冗余阵列

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Sparse arrays have received considerable attention due to their capability of resolving O(N2) uncorrelated sources with N physical sensors, unlike the uniform linear array (ULA) which identifies at most N - 1 sources. This is because sparse arrays have an O(N2)-long ULA segment in the difference coarray, defined as the set of differences between sensor locations. Among the existing array configurations, minimum redundancy arrays (MRA) have the largest ULA segment in the difference coarray with no holes. However, in practice, ULA is robust, in the sense of coarray invariance to sensor failure, but MRA is not. This paper proposes a novel array geometry, named as the robust MRA (RMRA), that maximizes the size of the hole-free difference coarray subject to the same level of robustness as ULA. The RMRA can be found by solving an integer program, which is computationally expensive. Even so, it will be shown that the RMRA still owns O(N2) elements in the hole-free difference coarray. In particular, for sufficiently large N, the aperture for RMRA, which is approximately half of the size of the difference coarray, is bounded between 0.0625N2 and 0.2174N2.1.
机译：由于它们的解析o的能力，稀疏阵列受到了相当大的关注 2 ）与N个物理传感器的不相关来源，与均匀的线性阵列（ULA）不同，它识别至大多数N-1源。这是因为稀疏阵列有一个o（n 2 ） - 在差异Coarray中的leg ula段，定义为传感器位置之间的差异集。在现有的阵列配置中，最小冗余阵列（MRA）在没有孔的差异Coarray中具有最大的ULA段。然而，在实践中，ULA是强大的，在Coarray不变与传感器失败的感觉中，但MRA不是。本文提出了一种名为鲁棒MRA（RMRA）的新型阵列几何形状，其最大化无孔差异携带型勾勒的尺寸，如ula的相同稳健性。通过求解整数程序，可以找到RMRA，这是计算昂贵的。即便如此，将显示RMRA仍然拥有O（n 2 ）无空穴差异亚拉亚的元素。特别地，对于足够大的n，对于差异亚拉雷的大约一半的RMRA的孔径为0.0625N 2 和0.2174N. 2.1 。

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