Kalman Filtering in Radio Aperture Synthesis

这项研究的第一个目标是通过卡尔曼滤波空间频域中大气引起的相位波动来改善扩展源的射电天文图像。 通过时间序列分析获得每个天线对的大气相位噪声模型。 通过将 CLEAN 分量转换到空间频域、将它们置于与数据相同的时间基线顺序并对这些序列进行建模，可以从 CLEAN 分量获得无线电源模型。 两个模型都放入卡尔曼滤波器中，然后对数据进行过滤。 卡尔曼滤波器将区分有用信号和不需要的信号，从而产生损坏较少的数据集，可供映射。 第二个目标是通过将图像分割成统计上平稳的片段、对片段中的信号和噪声进行建模以及对每个片段进行卡尔曼滤波来减少图像中的噪声。 该领域的新工作将是整合改进的信息；滤波器模式下的信号和噪声处理。 一个例子是包含有色噪声。 这将减少背景噪声，并将减少天体弱信号部分的噪声。

The first objective of this research is to improve radio astronomy images of extended sources by Kalman filtering the atmospherically induced phase fluctuations in the spatial frequency domain. Models for the atmospheric-phase noise for each antenna pair are obtained with times series analysis. A model for the radio source is obtained from the CLEAN components by transforming them to the spatial frequency domain, putting them in the same time-baseline order as the data, and modeling these series. Both models are placed in the Kalman filter and then the data are subsequently filtered. The Kalman filter will discriminate between the wanted and unwanted signals, thereby producing a less corrupted data set which is ready for mapping. The second objective is to reduce the noise in the images by segmenting the image into statistically stationary segments, modeling the signal and the noise in the segments, and Kalman filtering each segment. New work in this area will be the incorporation of improved information; the signal and noise processes in the filter modes. One example is the inclusion of colored noise. This will reduce the background noise and will reduce the noise in the weak-signal portion of the astronomical object.