论文标题
嵌套阵列的非题盲校准，具有渐近性最佳加权
Non-Iterative Blind Calibration of Nested Arrays with Asymptotically Optimal Weighting
论文作者
论文摘要
传感器阵列的盲目校准（不使用校准信号）在阵列处理中是一个重要但充满挑战的问题。尽管已经为“经典”阵列结构（例如均匀的线性阵列）提出了许多方法，但在更“现代”的稀疏阵列的背景下发现了很多方法。在本文中，我们提出了一种新颖的盲目校准方法，其$ 2 $级别的嵌套阵列。具体而言，尽管最近在文献中提出了矛盾的主张，但我们表明，最小二乘方法实际上可以用这种阵列用于此目的。此外，LS方法对传感器的增益和相位偏移的最佳加权LS关节估计产生，这会导致更准确的校准，进而在后续估计任务（例如，范围的方向）中提高准确性。正如我们在模拟中所证明的那样，我们的方法可以扩展到$ k $级别的数组（$ k> 2 $），在准确性和计算效率方面都优于当前最新技术。
Blind calibration of sensors arrays (without using calibration signals) is an important, yet challenging problem in array processing. While many methods have been proposed for "classical" array structures, such as uniform linear arrays, not as many are found in the context of the more "modern" sparse arrays. In this paper, we present a novel blind calibration method for $2$-level nested arrays. Specifically, and despite recent contradicting claims in the literature, we show that the Least-Squares (LS) approach can in fact be used for this purpose with such arrays. Moreover, the LS approach gives rise to optimally-weighted LS joint estimation of the sensors' gains and phases offsets, which leads to more accurate calibration, and in turn, to higher accuracy in subsequent estimation tasks (e.g., direction-of-arrival). Our method, which can be extended to $K$-level arrays ($K>2$), is superior to the current state of the art both in terms of accuracy and computational efficiency, as we demonstrate in simulation.
