Interferometric Passive Radar Imaging with Deep Denoising Priors

TL;DR

This paper introduces a deep learning-based interferometric inversion method for passive radar imaging, leveraging spectral estimation, denoising priors, and iterative algorithms to improve image quality and efficiency in low-sample scenarios.

Contribution

It proposes a novel deep learning framework combining spectral estimation and denoising priors for passive radar imaging, enhancing reconstruction speed and quality.

Findings

Faster reconstruction compared to existing methods.

Superior image quality in low-sample regimes.

Effective use of CNN-based denoisers in simulated passive SAR data.

Abstract

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.