Radio-Astronomy Imaging and Interference Excision Using Tensor Decomposition and Canonical Correlation Analysis

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.