  Passive radar has key advantages over its active counterpart in terms of cost
and stealth. In this paper, we address passive radar imaging problem by
interferometric inversion using a spectral estimation method with a priori
information within a deep learning (DL) framework. Cross-correlating the
received signals from different look directions mitigates the influence of
shared transmitter related phase components despite lack of a cooperative
transmitter, and permits tractable inference via interferometric inversion. We
thereon leverage deep architectures for modeling a priori information and for
improving sample efficiency of state-of-the-art methods. Our approach comprises
of an iterative algorithm based on generalizing the power method, and applies
denoisers using plug-and-play (PnP) and regularization by denoising (RED)
techniques. We evaluate our approach using simulated data for passive synthetic
aperture radar (SAR) by using convolutional neural networks (CNN) as denoisers.
The numerical experiments show that our method can achieve faster
reconstruction and superior image quality in sample starved regimes than the
state-of-the-art passive interferometric imaging algorithms.
