一种寻找最优鲁棒最小冗余阵列（RMRAs）的成功即跃进穷举搜索方法：针对11至20个传感器的新阵列配置
A Leap-on-Success Exhaustive Search Method to Find Optimal Robust Minimum Redundancy Arrays (RMRAs): New Array Configurations for Sensor Counts 11 to 20
AI总结：
本文提出了一种Leap-on-Success穷举搜索算法，成功发现了11至15个传感器规模的最优鲁棒最小冗余阵列（RMRAs）配置及16至20的近最优配置，有效解决了该NP-hard问题并提升了阵列在单传感器故障下的鲁棒性。
摘要：
双重冗余稀疏阵列（TFRAs）旨在即使在单个传感器发生故障的情况下也能保持准确的方向估计，这得益于其设计中注入的刻意共阵列冗余。鲁棒最小冗余阵列（RMRAs）是TFRAs的一个专门类别，它优化这种冗余，以在给定传感器数量下实现最大可能的孔径。然而，寻找最优的RMRA配置是一个NP-hard问题，先前的研究仅报告了最多十个传感器的阵列的最优解。本文提出了针对11至15个阵列规模的新发现的最优RMRA配置，这些配置是使用一种新颖的Leap-on-Success穷举搜索算法识别的，该算法通过在找到最优解时终止搜索来有效减少计算量。这些阵列的鲁棒性在所有单元素故障场景下通过MATLAB仿真进行了验证，证实了与一些在特定传感器位置容易发生故障的现有TFRAs相比，它们具有卓越的弹性。此外，本文还报告了针对16至20个阵列规模的近最优配置，突出了在计算资源充足的情况下，所提出方法在更大阵列设计中的潜在适用性。这项工作不仅推进了RMRA设计的最新技术，还引入了一种有效的搜索方法，可用于未来在阵列配置优化中的探索。
英文摘要：
Two-fold redundant sparse arrays (TFRAs) are designed to maintain accurate direction estimation even in the event of a single sensor failure, leveraging the deliberate coarray redundancy infused into their design. Robust Minimum Redundancy Arrays (RMRAs), a specialized class of TFRAs, optimize this redundancy to achieve the maximum possible aperture for a given number of sensors. However, finding optimal RMRA configurations is an NP-hard problem, with prior research reporting optimal solutions only for arrays of up to ten sensors. This paper presents newly discovered optimal RMRA configurations for array sizes 11 to 15, identified using a novel Leap-on-Success exhaustive search algorithm that efficiently reduces computational effort by terminating the search upon locating optimal solutions. The robustness of these arrays was validated under all single-element failure scenarios using MATLAB simulations, confirming their superior resilience compared to some existing TFRAs vulnerable to failures at specific sensor positions. Furthermore, near-optimal configurations for array sizes 16 to 20 are also reported, highlighting the potential applicability of the proposed method for larger array designs given sufficient computational resources. This work not only advances the state-of-the-art in RMRA design but also introduces an effective search methodology that can be leveraged for future explorations in array configuration optimization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
