Radio-Astronomy Imaging and Interference Excision Using Tensor Decomposition and Canonical Correlation Analysis
Radio-Astronomy Imaging and Interference Excision Using Tensor Decomposition and Canonical Correlation Analysis
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DOI：
10.1109/icassp49357.2023.10096289
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Mikael Sørensen;N. Sidiropoulos
中科院分区：
文献类型：
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作者：
Mikael Sørensen;N. Sidiropoulos
具有大量传感器的天线阵列在射电天文学中越来越常见。这激发了阵列信号处理工具的发展，利用源信号的属性，如稀疏性和频谱或时间的变化，高分辨率成像。我们提出了一个新的多频协方差矩阵模型的射电天文成像，利用光谱变化的天文源。我们表明，张量分解方法可用于计算高分辨率的天文场景，包括Q点源的图像。在这种情况下，张量分解可以减少问题更简单的单点源成像问题。我们还解释了如何典型相关分析可以用来减轻甚至完全消除（未知）窄带干扰源的影响，这是在这种情况下的一个关键挑战。
Antenna arrays with a large number of sensors are becoming increasingly common in radio astronomy. This has motivated the development of array signal processing tools for high-resolution imaging that exploit source signal properties such as sparsity and spectral or temporal variability. We propose a new multi-frequency covariance matrix model for radio astronomical imaging that exploits spectral variability of the astronomical sources. We show that tensor decomposition methods can be used to compute high-resolution images of astronomical scenes that comprise Q point sources. In this context, tensor decomposition can reduce the problem to simpler single-point source imaging problems. We also explain how canonical correlation analysis can be used to mitigate or even altogether remove the effect of (unknown) narrowband interference sources, which is a key challenge in this context.