论文标题
副阵列中的最低于点纠错自相关处理
Minimum Mean-Squared-Error Autocorrelation Processing in Coprime Arrays
论文作者
论文摘要
共阵阵列可实现排序方向（DOA）估计数量增加的来源。为此，接收器估计了较大的虚拟统一线性阵列（coArray）的自相关矩阵，通过将选择或平均应用于物理阵列的自相关估计值，然后进行空间平滑尺寸。选择和平均均已在没有最佳标准的情况下设计，并达到任意（次优）均方越（MSE）估计性效果。在这项工作中，我们设计了一个新型的副阵列接收器，该接收器估计了用最小MSE（MMSE）的共同相关性，以供源DOA的任何概率分布。我们广泛的数值评估表明，所提出的MMSE方法返回出色的自相关估计值，这反过来又可以使DOA估计性能较高，而不是标准的估计性估计。
Coprime arrays enable Direction-of-Arrival (DoA) estimation of an increased number of sources. To that end, the receiver estimates the autocorrelation matrix of a larger virtual uniform linear array (coarray), by applying selection or averaging to the physical array's autocorrelation estimates, followed by spatial-smoothing. Both selection and averaging have been designed under no optimality criterion and attain arbitrary (suboptimal) Mean-Squared-Error (MSE) estimation performance. In this work, we design a novel coprime array receiver that estimates the coarray autocorrelations with Minimum-MSE (MMSE), for any probability distribution of the source DoAs. Our extensive numerical evaluation illustrates that the proposed MMSE approach returns superior autocorrelation estimates which, in turn, enable higher DoA estimation performance compared to standard counterparts.
