Zobrazeno 1 - 10
of 96
pro vyhledávání: '"Yazıcı, Birsen"'
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
Externí odkaz:
http://arxiv.org/abs/2306.17096
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
Externí odkaz:
http://arxiv.org/abs/2212.01940
Robustness to noise and outliers is a desirable trait in phase retrieval algorithms for many applications in imaging and signal processing. In this paper, we develop novel robust phase retrieval algorithms based on the minimization of reverse Kullbac
Externí odkaz:
http://arxiv.org/abs/2204.09791
We introduce a deep learning (DL) based network and an associated exact recovery theory for imaging from intensity-only measurements. The network architecture uses a recurrent structure that unrolls the Wirtinger Flow (WF) algorithm with a deep decod
Externí odkaz:
http://arxiv.org/abs/2108.01735
In this paper, we present an approach for ground moving target imaging (GMTI) and velocity recovery using synthetic aperture radar. We formulate the GMTI problem as the recovery of a phase-space reflectivity (PSR) function which represents the streng
Externí odkaz:
http://arxiv.org/abs/2105.02081
Autor:
Yonel, Bariscan, Yazici, Birsen
In this paper, we develop a novel framework to optimally design spectral estimators for phase retrieval given measurements realized from an arbitrary model. We begin by deconstructing spectral methods, and identify the fundamental mechanisms that inh
Externí odkaz:
http://arxiv.org/abs/2012.01652
Autor:
Yonel, Bariscan, Yazici, Birsen
Publikováno v:
IEEE Transactions on Signal Processing, 2020, Vol.68
In this paper, we analyze the non-convex framework of Wirtinger Flow (WF) for phase retrieval and identify a novel sufficient condition for universal exact recovery through the lens of low rank matrix recovery theory. Via a perspective in the lifted
Externí odkaz:
http://arxiv.org/abs/2001.02855
Publikováno v:
IEEE Transactions on Computational Imaging, 2020, Vol. 6
In this paper, we present a novel approach that can exactly recover extended targets in wave-based multistatic interferometric imaging, based on Generalized Wirtinger Flow (GWF) theory [1]. Interferometric imaging is a generalization of phase retriev
Externí odkaz:
http://arxiv.org/abs/1905.10459
Autor:
Yonel, Bariscan, Yazici, Birsen
Publikováno v:
SIAM J. Imaging Sci., 2019, 12.4
Interferometric inversion involves recovery of a signal from cross-correlations of its linear transformations. A close relative of interferometric inversion is the generalized phase retrieval problem, which consists of recovering a signal from the au
Externí odkaz:
http://arxiv.org/abs/1901.03940
We consider a bistatic configuration with a stationary transmitter transmitting unknown waveforms of opportunity and a moving receiver, and present a Deep Learning (DL) framework for passive synthetic aperture radar (SAR) imaging. Existing passive ra
Externí odkaz:
http://arxiv.org/abs/1809.04768