Optimizing Low-Frequency Mode Stirring Performance Using Principal Component Analysis
Optimizing Low-Frequency Mode Stirring Performance Using Principal Component Analysis
复制标题
使用主成分分析优化低频模式搅拌性能
DOI：
10.1109/temc.2013.2271903
复制
发表时间：
2014
影响因子：
2.1
通讯作者：
L. R. Arnaut
中科院分区：
文献类型：
--
作者：
L. R. Arnaut
我们根据在混响室内传感器的单个位置跨频段收集的调谐器扫描数据，制定并执行机械搅拌场的主成分分析 (PCA)。在欠调制和过调制状态下执行基于协方差和相关性的 PCA 并进行相互比较。证明了搅拌性能作为搅拌器角位置的函数的非平稳性。结果表明，这种不均匀性可以量化并利用来选择一组最佳搅拌角。发现旋转的主成分可解释为由搅拌器的特定角扇区搅拌的能量，并且与数据的相关结构相关。该分析引出了特征搅拌（搅拌模式）的概念，它形成了一组正交的经验基函数，用于扩展搅拌数据。
We formulate and perform principal component analysis (PCA) of mechanically stirred fields, based on tuner sweep data collected across a frequency band at a single location of a sensor inside a reverberation chamber. Both covariance- and correlation-based PCA in undermoded and overmoded regime are performed and intercompared. The nonstationarity of the stir performance as a function of the angular position of the stirrer is demonstrated. It is shown that this nonuniformity can be quantified and exploited to select a set of optimal stir angles. The rotated principal components are found to be interpretable as energy stirred by specific angular sectors of the stirrer and are related to the correlation structure of the data. The analysis leads to the concept of eigen-stirrings (stir modes), which form an orthonormal set of empirical basis functions for expanding stir data.