加权渐近最优的嵌套阵列非迭代盲校准
Non-Iterative Blind Calibration of Nested Arrays with Asymptotically Optimal Weighting
摘要：
传感器阵列的盲校准（不使用校准信号）是阵列处理中一个重要而又具有挑战性的问题。虽然已经提出了许多方法用于“经典”阵列结构，如均匀线性阵列，但在更“现代”的稀疏阵列的背景下发现的方法并不多。本文提出了一种新的$2$级嵌套阵列的盲校准方法。具体来说，尽管最近在文献中的矛盾的索赔，我们表明，最小二乘（LS）的方法，实际上可以用于此目的与这样的阵列。此外，LS方法引起传感器的增益和相位偏移的最优加权LS联合估计，这导致更准确的校准，并且进而导致后续估计任务（例如，到达方向）。我们的方法，它可以扩展到$K$级阵列（$K>2$），是优于目前的技术水平，无论是在精度和计算效率方面，我们在模拟中证明。
英文摘要：
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.
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。
