62,018 research outputs found
A sparsity-driven approach for joint SAR imaging and phase error correction
Image formation algorithms in a variety of applications have explicit or implicit dependence on a mathematical model of the observation process. Inaccuracies in the observation model may cause various degradations and artifacts in the reconstructed images. The application of interest in this paper is synthetic aperture radar (SAR) imaging, which particularly suffers from motion-induced model errors. These types of errors result in phase errors in SAR data which cause defocusing of the reconstructed images. Particularly focusing on imaging of fields that admit a sparse representation, we propose a sparsity-driven method for joint SAR imaging and phase error correction. Phase error correction is performed during the image formation process. The problem is set up as an optimization problem in a nonquadratic regularization-based framework. The method involves an iterative algorithm each iteration of which
consists of consecutive steps of image formation and model error correction. Experimental results show the effectiveness of the approach for various types of phase errors, as well as the improvements it provides over existing techniques for model error compensation in SAR
An Extended Virtual Aperture Imaging Model for Through-the-wall Sensing and Its Environmental Parameters Estimation
Through-the-wall imaging (TWI) radar has been given increasing attention in recent years. However, prior knowledge about environmental parameters, such as wall thickness and dielectric constant, and the standoff distance between an array and a wall, is generally unavailable in real applications. Thus, targets behind the wall suffer from defocusing and displacement under the conventional imag¬ing operations. To solve this problem, in this paper, we first set up an extended imaging model of a virtual aperture obtained by a multiple-input-multiple-output array, which considers the array position to the wall and thus is more applicable for real situations. Then, we present a method to estimate the environmental parameters to calibrate the TWI, without multiple measurements or dominant scatter¬ers behind-the-wall to assist. Simulation and field experi¬ments were performed to illustrate the validity of the pro¬posed imaging model and the environmental parameters estimation method
A sparsity-driven approach for joint SAR imaging and phase error correction
Image formation algorithms in a variety of applications have explicit or implicit dependence on a mathematical model of the observation process. Inaccuracies in the observation model may cause various degradations and artifacts in the reconstructed images. The application of interest in this paper is synthetic aperture radar (SAR) imaging, which particularly suffers from motion-induced model errors. These types of errors result in phase errors in SAR data which cause defocusing of the reconstructed images. Particularly focusing on imaging of fields that admit a sparse representation, we propose a sparsity-driven method for joint SAR imaging and phase error correction. Phase error correction is performed during the image formation process. The problem is set up as an optimization problem in a nonquadratic regularization-based framework. The method involves an iterative algorithm each iteration of which
consists of consecutive steps of image formation and model error correction. Experimental results show the effectiveness of the approach for various types of phase errors, as well as the improvements it provides over existing techniques for model error compensation in SAR
Microwave Radar-Based Breast Cancer Detection:Imaging in Inhomogeneous Breast Phantoms
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OFDM Synthetic Aperture Radar Imaging with Sufficient Cyclic Prefix
The existing linear frequency modulated (LFM) (or step frequency) and random
noise synthetic aperture radar (SAR) systems may correspond to the frequency
hopping (FH) and direct sequence (DS) spread spectrum systems in the past
second and third generation wireless communications. Similar to the current and
future wireless communications generations, in this paper, we propose OFDM SAR
imaging, where a sufficient cyclic prefix (CP) is added to each OFDM pulse. The
sufficient CP insertion converts an inter-symbol interference (ISI) channel
from multipaths into multiple ISI-free subchannels as the key in a wireless
communications system, and analogously, it provides an inter-range-cell
interference (IRCI) free (high range resolution) SAR image in a SAR system. The
sufficient CP insertion along with our newly proposed SAR imaging algorithm
particularly for the OFDM signals also differentiates this paper from all the
existing studies in the literature on OFDM radar signal processing. Simulation
results are presented to illustrate the high range resolution performance of
our proposed CP based OFDM SAR imaging algorithm.Comment: This version has been accepted by IEEE Transactions on Geoscience and
Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing 201
Range-Point Migration-Based Image Expansion Method Exploiting Fully Polarimetric Data for UWB Short-Range Radar
Ultrawideband radar with high-range resolution is a promising technology for use in short-range 3-D imaging applications, in which optical cameras are not applicable. One of the most efficient 3-D imaging methods is the range-point migration (RPM) method, which has a definite advantage for the synthetic aperture radar approach in terms of computational burden, high accuracy, and high spatial resolution. However, if an insufficient aperture size or angle is provided, these kinds of methods cannot reconstruct the whole target structure due to the absence of reflection signals from large part of target surface. To expand the 3-D image obtained by RPM, this paper proposes an image expansion method by incorporating the RPM feature and fully polarimetric data-based machine learning approach. Following ellipsoid-based scattering analysis and learning with a neural network, this method expresses the target image as an aggregation of parts of ellipsoids, which significantly expands the original image by the RPM method without sacrificing the reconstruction accuracy. The results of numerical simulation based on 3-D finite-difference time-domain analysis verify the effectiveness of our proposed method, in terms of image-expansion criteria
IRCI Free Range Reconstruction for SAR Imaging with Arbitrary Length OFDM Pulse
Our previously proposed OFDM with sufficient cyclic prefix (CP) synthetic
aperture radar (SAR) imaging algorithm is inter-range-cell interference (IRCI)
free and achieves ideally zero range sidelobes for range reconstruction. In
this OFDM SAR imaging algorithm, the minimum required CP length is almost equal
to the number of range cells in a swath, while the number of subcarriers of an
OFDM signal needs to be more than the CP length. This makes the length of a
transmitted OFDM sequence at least almost twice of the number of range cells in
a swath and for a wide swath imaging, the transmitted OFDM pulse length becomes
long, which may cause problems in some radar applications. In this paper, we
propose a CP based OFDM SAR imaging with arbitrary pulse length, which has IRCI
free range reconstruction and its pulse length is independent of a swath width.
We then present a novel design method for our proposed arbitrary length OFDM
pulses. Simulation results are presented to illustrate the performances of the
OFDM pulse design and the arbitrary pulse length CP based OFDM SAR imaging.Comment: 29 pages, 10 figures, regular pape
2-D Coherence Factor for Sidelobe and Ghost Suppressions in Radar Imaging
The coherence factor (CF) is defined as the ratio of coherent power to
incoherent power received by the radar aperture. The incoherent power is
computed by the multi-antenna receiver based on only the spatial variable. In
this respect, it is a one-dimensional (1-D) CF, and thereby the image sidelobes
in down-range cannot be effectively suppressed. We propose a two-dimensional
(2-D) CF by supplementing the 1-D CF by an incoherent sum dealing with the
frequency dimension. In essence, we employ both spatial diversity and frequency
diversity which, respectively, enhance imaging quality in cross range and
range. Simulations and experimental results are provided to demonstrate the
performance advantages of the proposed approach.Comment: 7 pages, 21 figure
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