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keyboard_arrow_downTitleAbstractIntroductionAll TopicsEngineeringCivil Engineeringdownload

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Download Free PDFREADING AND INTERPRETING CONSTRUCTION DRAWINGS Reading and Interpreting Construction Drawings, Course #403 Presented byjon doevisibility

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Abstract

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This document outlines the essential principles and techniques for reading and interpreting construction drawings, which are vital for conveying architectural and engineering designs of construction projects. It distinguishes between pictorial drawings, used for presentations, and orthographic projections that provide detailed views necessary for construction. The content covers measurement tools, lines and symbols, different types of drawings, and provides guidance on visualization and interpretation skills that are crucial for understanding construction documents.

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2013 IEEE International Conference on Image Processing, 2013

The radar data to form synthetic aperture radar (SAR) imagery is normally transmitted and received by moving platforms like aircraft or vehicles. In many situations, the platforms move at high speed; which reduces the number of sampling records collected to the synthetic aperture, hence degrades the quality of the reconstructed SAR images. Therefore, it is necessary to develop an algorithm that is capable of increasing the temporal frequency rate of the received data. In this paper, we propose a novel technique to generate intermediate records from the existing ones by a locally-adaptive low-rank matrix recovery framework. The system first fills in the blank records using a bi-directional motion estimation scheme. The initialized aperture records are then refined by a robust low-rank matrix completion algorithm using the reference from neighborhood clean records. Experiments demonstrate that the proposed method provides comparative results when up-converting the aperture rate by a factor of two or four, both in mean square error of the raw SAR signal and PSNR performance of the recovered SAR images.

downloadDownload free PDFView PDFchevron_rightSar/Isar Advanced Image Reconstruction AlgorithmsDimitar Minchev

2018

In this work SAR/ISAR (Inverse Synthetic Aperture Radar/Inverse Synthetic Aperture Radar) parametric image reconstruction concepts are discussed. First, an image reconstruction procedure based on l0 norm optimization is developed and applied over reduced number of measurements defined by randomly generated azimuth and range sensing matrices. Second, Kalman algorithm is applied for ISAR image extraction. Vector measurement and state equations are derived. Approximation functions based on LFM signal are defined. Results of numerical experiments are presented.

downloadDownload free PDFView PDFchevron_rightImage reconstruction from Fourier transform magnitude with applications to synthetic aperture radar imagingGilda Schirinzi, Rocco Pierri

Journal of the Optical Society of America A, 1996

The problem of reconstructing an unknown signal starting from the knowledge of only magnitude information about its Fourier transform is addressed. To this end a phase retrieval (PR) method, based on the inversion of a quadratic operator, is proposed, presented, and discussed. The main feature of the approach is the use of a square amplitude distribution rather than an amplitude distribution. In the lack of any a priori information about the phase to be recovered, the success of the method is related to the availability of a sufficiently large ratio between the dimension of data and the dimension of unknowns; the retrieval procedure could converge to a meaningless trap when this ratio is not large enough. It should be noted, however, that the proposed method has a range of global effectiveness wider than that of previous approaches. Moreover, when available, the use of a priori information, such as the knowledge of the support of the scene to be imaged or the knowledge of a part of the scene, allows one to achieve accurate final images starting from a completely random initial guess of the image values. We apply our PR-based method to a synthetic aperture radar (SAR) case. As is well known, random motion of the platform and /or propagation effects in turbulent random media can affect the correct phase synchronization of the received SAR raw data needed for well-focused SAR images. As a matter of fact, even relatively small phase errors greatly impair the quality of the image. Under common conditions phase errors on the received signal affect just the phase of the Fourier transform of the intensity image without impairing its amplitude. The proposed technique is able to compensate these phase errors by retrieving the phase of the Fourier transform of the image intensity. We also present several experiments performed either on numerically simulated data or on actual data relative to an airborne SAR mission that show the effectiveness of the proposed PR method.

downloadDownload free PDFView PDFchevron_right<title>Joint space aspect reconstruction of wide-angle SAR exploiting sparsity</title>Mujdat Cetin

Algorithms for Synthetic Aperture Radar Imagery XV, 2008

In this paper we present an algorithm for wide-angle synthetic aperture radar (SAR) image formation. Reconstruction of wide-angle SAR holds a promise of higher resolution and better information about a scene, but it also poses a number of challenges when compared to the traditional narrow-angle SAR. Most prominently, the isotropic point scattering model is no longer valid. We present an algorithm capable of producing high resolution reflectivity maps in both space and aspect, thus accounting for the anisotropic scattering behavior of targets. We pose the problem as a non-parametric three-dimensional inversion problem, with two constraints: magnitudes of the backscattered power are highly correlated across closely spaced look angles and the backscattered power originates from a small set of point scatterers. This approach considers jointly all scatterers in the scene across all azimuths, and exploits the sparsity of the underlying scattering field. We implement the algorithm and present reconstruction results on realistic data obtained from the XPatch Backhoe dataset.

downloadDownload free PDFView PDFchevron_rightSAR Image Superresolution via 2-D Adaptive ExtrapolationSergio Cabrera

Radar Signal Processing and Its Applications, 2003

In this paper, we present a description of a nonparametric two dimensional (2-D) procedure to extrapolate a signal, an extension of the Adaptive Weighted Norm Extrapolation (AWNE) method, and illustrate its application to SAR image formation. The benefits of the AWNE procedure are shown for synthetic data and for MSTAR data. Once the phase history is recovered, the AWNE method is applied to a subaperture or to the full set of frequency samples to extrapolate them to a larger aperture from which a superresolved complex SAR image is obtained. Use of the 2-D AWNE procedure proves to be superior to its one-dimensional separable version by reducing undesirable effects such as sidelobe interference, and variability in energy of the extrapolated data from row to row and column to column. To assess the performance of AWNE in enhancing prominent scatterers, reducing speckle, and suppressing clutter, we compare the superresolved images to the images formed with the traditional Fourier technique starting from the same phase history data. Fourier images are also compared with superresolved images formed using less data in order to assess the quality of the extrapolation and to quantify the method's ability to recover lost resolution. We illustrate performance by visual comparison and by the use of a geometric constellation of prominent point scatterers of the targets extracted from the images. A brief comparison with the 2-D versions of Capon and the linear prediction methods is illustrated and a hybrid AWNE/Capon approach is proposed.

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Iet Radar Sonar and Navigation, 2017

Three-dimensional (3D) target reconstruction from inverse synthetic aperture radar (ISAR) data has a wide application in target scattering modelling, detection, and identification. In ISAR imaging of targets with complex motions such as the non-cooperative manoeuvring targets, the scattering centres on the target may rotate slowly in 3D space during the observation time. In this study, the authors have developed a new formulation for 3D target geometry reconstruction from the scattering centres high-resolution range (HRR) measurements, based on target motion features. First, after the translation compensation, the multi-view HRR of the scattering centres is extracted by HR spectral estimation technique. Then, the multiview measurements data without correspondence information are associated using the multiple hypotheses tracking algorithm. Finally, the 3D target geometry and motion are reconstructed from the singular value decomposition of the correlated HRR data matrix. The effectiveness of the proposed algorithm is demonstrated by both simulated and real data experiment results.

downloadDownload free PDFView PDFchevron_rightNovel methods for SAR imaging problemsSalih Ugur

2013

Synthetic Aperture Radar (SAR) provides high resolution images of terrain reflectivity. SAR systems are indispensable in many remote sensing applications. High resolution imaging of terrain requires precise position information of the radar platform on its flight path. In target detection and identification applications, imaging of sparse reflectivity scenes is a requirement. In this thesis, novel SAR image reconstruction techniques for sparse target scenes are developed. These techniques differ from earlier approaches in their ability of simultaneous image reconstruction and motion compensation. It is shown that if the residual phase error after INS/GPS corrected platform motion is captured in the signal model, then the optimal autofocused image formation can be formulated as a sparse reconstruction problem. In the first proposed technique, Non-Linear Conjugate Gradient Descent algorithm is used to obtain the optimum reconstruction. To increase robustness in the reconstruction, Total Variation penalty is introduced into the cost function of the optimization. To reduce the rate of A/D conversion and memory requirements, a specific under sampling pattern is introduced. In the second proposed technique, Expectation Maximization Based Matching Pursuit (EMMP) algorithm is utilized to obtain the optimum sparse SAR reconstruction. EMMP algorithm is greedy and computationally less complex resulting in fast SAR image reconstructions. Based on a variety of metrics, performances of the proposed techniques are compared. It is observed that the EMMP algorithm has an additional advantage of reconstructing off-grid targets by perturbing on-grid basis vectors on a finer grid.

downloadDownload free PDFView PDFchevron_rightSub-aperture method for the wide-bandwidth wide-angle inverse synthetic aperture radar imagingÖzkan Kirik, Caner Ozdemir

Electrical and Electronics Engineering, …

In this paper, a method for obtaining focused inverse synthetic aperture radar (ISAR) images of targets based on the radar backscattering measurements taken over wide bands and wide angles [1]. The proposed method divides wide angle and wide frequency band into small aperture bands in the spatial frequency or Fourier domain. This setup makes it possible to use fast calculation of ISAR images for every sub-aperture data set as in the case of standard ISAR case of small-bandwidth and small-angle. The details of the method are presented and numerical examples are given for the validation the method. The electromagnetic scattering estimation from the target is calculated via a hybrid simulator that uses both the physical optics [1-3] and the shooting & bouncing ray concepts [4].

downloadDownload free PDFView PDFchevron_rightTwo-dimensional scattering center extraction using super-resolution techniques [inverse SAR applicationsChen Jianwen

2004

The concept of scattering centers on a target is commonly used for radar signature modeling and data compression, as well as target recognition. In particular, two-dimensional (2-D) scattering centers are useful features in automatic target recognition, which uses a synthetic aperture radar system, because they are directly related to physical scattering mechanisms and also have small dimensionality. In this paper, we propose a new technique for estimating 2-D scattering centers using radar data in the frequency-aspect domain. The technique first estimates one-dimensional scattering centers at several aspects, and the multiple elastic modules network optimization is exploited to find 2-D locations and amplitudes of the target's scattering centers. Experimental results illustrate that the proposed method is efficient not only for estimating 2-D scattering centers on the target but also in computation.

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Reshetov LA A sparse sampling strategy for angular superresolution of real beam scanning radarЛеонид Решетов

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downloadDownload free PDFView PDFchevron_rightUsing auto-regression extrapolation algorithms in high-resolution imaging radarMaarten Van der Goot

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The resolution of images obtained by Inverse Synthetic Aperture Radar (ISAR) is mainly determined by the bandwidth and angular span of the recorded data. Conventional imaging techniques do not use the available data very efficient: the application of a Fast Fourier Transform implies a limited resolution and the loss of valuable data in the focusing step. In this report, the use of a data extrapolation technique is proposed. By means of the Burg method, the extrapolation coefficients of the linear auto-regression model are calculated. The quality of the extrapolation with this model is investigated with various signals and model orders. The results are compared with the characteristics of the data of variou~ objects in ISAR. It is concluded that the extrapolation technique will provide the best results when it is applied to the rectangular data grid that is obtained after focusing of the data. Extrapolation of the frequency axes gives a moderate quality, while extrapolation of the an...

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downloadDownload free PDFView PDFchevron_rightSynthetic-aperture radar processing using fast factorized back-projectionGunnar Stenstrom

IEEE Transactions on Aerospace and Electronic Systems, 2003

Exact synthetic aperture radar (SAR) inversion for a linear aperture may be obtained using fast transform techniques. Alternatively, back-projection integration in time domain can also be used. This technique has the benefit of handling a general aperture geometry. In the past, however, back-projection has seldom been used due to heavy computational burden. We show that the back-projection integral can be recursively partitioned and an effective algorithm constructed based on aperture factorization. By representing images in local polar coordinates it is shown that the number of operations is drastically reduced and can be made to approach that of fast transform algorithms. The algorithm is applied to data from the airborne ultra-wideband CARABAS SAR and shown to give a reduction in processing time of two to three orders of magnitude.

downloadDownload free PDFView PDFchevron_rightAdvanced image formation and processing of partial synthetic aperture radar dataPatrice Abry

IET Signal Processing, 2012

We propose an advanced synthetic aperture radar (SAR) image formation framework based on iterative inversion algorithms that approximately solve a regularised least squares problem. The framework provides improved image reconstructions, compared to the standard methods, in certain imaging scenarios, e.g. when the SAR data are under-sampled. Iterative algorithms also allow prior information to be used to solve additional problems such as the correction of unknown phase errors in the SAR data. Though, for an iterative inversion framework to be feasible, fast algorithms for the generative model and its adjoint must be available. We demonstrate how fast, N 2 log 2 N complexity, (re/back)-projection algorithms can be used as accurate approximations for the generative model and its adjoint, without the limiting geometric approximations of other N 2 log 2 N methods, e.g. the polar format algorithm. Experimental results demonstrate the effectiveness of our framework using publicly available SAR datasets.

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