论文标题
副阵列的结构化自相关矩阵估计
Structured Autocorrelation Matrix Estimation for Coprime Arrays
论文作者
论文摘要
副阵列接收器处理了接收信号快照的集合，以估计较大（虚拟）统一线性阵列的自相关矩阵（称为coarray）。通过接收信号模型，该矩阵必须为（i）正排定，（ii）Hermitian，（iii）toeplitz和（iv）其噪声 - 空间特征值必须相等。现有的同时自相关矩阵估计满足上述条件的子集。在这项工作中，我们提出了一个优化框架，该框架提供了满足所有四个条件的新颖估计。数值研究表明，在自相关矩阵估计误差和到达方向估计中，提出的估计值优于标准对应物。
A coprime array receiver processes a collection of received-signal snapshots to estimate the autocorrelation matrix of a larger (virtual) uniform linear array, known as coarray. By the received-signal model, this matrix has to be (i) Positive-Definite, (ii) Hermitian, (iii) Toeplitz, and (iv) its noise-subspace eigenvalues have to be equal. Existing coarray autocorrelation matrix estimates satisfy a subset of the above conditions. In this work, we propose an optimization framework which offers a novel estimate satisfying all four conditions. Numerical studies illustrate that the proposed estimate outperforms standard counterparts, both in autocorrelation matrix estimation error and Direction-of-Arrival estimation.
