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Found 7 papers, 0 papers with code
Deep Denoising Prior-Based Spectral Estimation for Phaseless Synthetic Aperture Radar
Incoherent processing for synthetic aperture radar (SAR) is a promising approach that enables low implementation costs, simplified hardware designs and operations in high frequency spectrum compared to the conventional imaging methods using coherent processing.
Interferometric Passive Radar Imaging with Deep Denoising Priors
Passive radar has key advantages over its active counterpart in terms of cost and stealth.
Robust Phase Retrieval via Reverse Kullback-Leibler Divergence
In this paper, we develop novel robust phase retrieval algorithms based on the minimization of reverse Kullback-Leibler divergence (RKLD) within the Wirtinger Flow (WF) framework.
Unrolled Wirtinger Flow with Deep Decoding Priors for Phaseless Imaging
Furthermore, it facilitates simultaneous learning of the parameters of the decoding and encoding networks and the RNN.
Phase-Space Function Recovery for Moving Target Imaging in SAR by Convex Optimization
In this paper, we present an approach for ground moving target imaging (GMTI) and velocity recovery using synthetic aperture radar.
A Spectral Estimation Framework for Phase Retrieval via Bregman Divergence Minimization
To this end, we derive spectral methods that perform approximate minimization of KL-divergence, and the Itakura-Saito distance over phaseless measurements by using element-wise sample processing functions.
Deep Learning for Passive Synthetic Aperture Radar
Specifically, we design a recurrent neural network (RNN) architecture as an inverse solver based on the iterations of proximal gradient descent optimization methods.
