Learning-based probabilistic subarray switching for robust low-cost interferometric imaging

Computational cost poses a significant challenge in next-generation interferometric imaging systems. In these systems, the large number of antennas makes it impractical to process all measurements simultaneously due to computational capacity constraints. To reduce the computational burden while preserving image reconstruction quality, we propose a subarray switching strategy that utilizes fewer antennas and different antenna configurations. To take into consideration the influence of the image reconstruction algorithm on the design of the subarray switching pattern and to fully exploit the flexibility of the switching strategy, we propose a probabilistic deep learning-based method for designing antenna switching patterns, named by Probabilistic Antenna Switcher (PAS). In addition to the computational challenge, interferometric systems are also particularly sensitive to the presence of radio frequency interferences (RFI), which heavily affects imaging quality. In order to address this issue, we show that it is possible to combine the proposed PAS with a RFI detection module. Specifically, this module is a neural network that is trained to identify and minimize the impact of RFI-affected antennas in the subarray selection process. This results in a RFI-aware PAS (RaPAS) , which balances computational efficiency, imaging quality, and robustness against RFI.