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Signal Processing Magazine, IEEE

Issue 4 • Date July 2014

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Displaying Results 1 - 24 of 24
  • [Front cover]

    Page(s): C1
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  • Table of contents

    Page(s): 1
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  • Staff Listing

    Page(s): 2
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  • Signal Processing: Is it Time to Change the Society's Name? [From the Editor]

    Page(s): 4
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  • At the Forefront in Technical Publications [President's Message]

    Page(s): 6
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  • Top Downloads in IEEE Xplore [Reader's Choice]

    Page(s): 8 - 9
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  • Signal Processing: On the Edge of Astronomy's New Frontier [Special Reports]

    Page(s): 10 - 12
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    Digital signal processing (DSP) plays several important roles in modern radio astronomy, such as processing data to create highresolution radio images, isolating weak emissions from celestial sources, and reducing distortions in incoming signals. DSP can also manage beamforming, a complex process that allows radio signals to be received from across the sky from any direction, and even multiple directions simultaneously. Many astronomy projects are now relying on advanced signal processing techniques to probe the edges of the universe to extend humanity's understanding of its origins, scan the skies to image exoplanets-worlds outside our solar system-and investigate many of the major outstanding questions in astronomy. Here's a look at two of them. View full abstract»

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  • Health Alliance Boosts Influence In Standards Development [Special Reports]

    Page(s): 13 - 148
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    Despite barriers relating to regulation, patient acceptance, and privacy concerns, market researchers estimate the growth of the mobile health-care market at US$9 billion in 2014. Looking ahead, analysts are projecting growth in the mHealth sector at a compound annual growth rate of nearly 40% over the next six years. Equally impressive, mHealth has the potential to dramatically reduce the cost of health care. View full abstract»

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  • Recent Advances in Radar Imaging [From the Guest Editors]

    Page(s): 15 - 158
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  • Wide-Angle Synthetic Aperture Radar Imaging: Models and algorithms for anisotropic scattering

    Page(s): 16 - 26
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    Advances in radar hardware have enabled the sensing of ever-wider synthesized apertures. In this article, radar video - a sequence of radar images indexed on subaperture - is discussed as a natural, convenient, and revealing representation to capture wide-angle scattering behavior of complex objects. We review the inverse problem of recovering wide-angle scene reflectivity from synthetic aperture radar (SAR) measurements, survey signal processing approaches for its solution, and introduce a novel Bayesian estimation method. Examples from measured and simulated scattering data are presented to illustrate scattering behavior conveniently revealed by the SAR video framework. View full abstract»

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  • Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing

    Page(s): 27 - 40
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    This article presents a survey of recent research on sparsity-driven synthetic aperture radar (SAR) imaging. In particular, it reviews 1) the analysis and synthesis-based sparse signal representation formulations for SAR image formation together with the associated imaging results, 2) sparsity-based methods for wide-angle SAR imaging and anisotropy characterization, 3) sparsity-based methods for joint imaging and autofocusing from data with phase errors, 4) techniques for exploiting sparsity for SAR imaging of scenes containing moving objects, and 5) recent work on compressed sensing (CS)-based analysis and design of SAR sensing missions. View full abstract»

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  • Tomographic Processing of Interferometric SAR Data: Developments, applications, and future research perspectives

    Page(s): 41 - 50
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    Synthetic aperture radar (SAR) data processed with interferometric techniques are widely used today for environmental risk monitoring and security. SAR tomography techniques are a recent advance that provide improved three-dimensional (3-D) reconstruction and long-term deformation monitoring capabilities. This article is meant to discuss the main developments achieved in the last few years in the SAR tomography framework, with particular reference to both urban and forest scenarios. An insight on classical multipass interferometric processing is also included to summarize the importance of the technology for natural hazards monitoring and to provide the basis for the description of SAR tomography. View full abstract»

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  • Superresolving SAR Tomography for Multidimensional Imaging of Urban Areas: Compressive sensing-based TomoSAR inversion

    Page(s): 51 - 58
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    With reference to the current status of VHR spaceborne tomographic SAR inversion presented in this article, the following conclusions can be drawn: VHR tomographic SAR inversion is able to reconstruct the shape and motion of individual buildings and entire city areas. SR is crucial and possible, e.g., using CS, for VHR tomographic SAR inversion for urban infrastructure. The motion or deformation of buildings is often nonlinear (periodic, accelerating, stepwise, etc.). Multicomponent nonlinear motion of multiple scatterers can be separated. The 4-D point clouds retrieved by VHR TomoSAR has a point density comparable to LiDAR and can be potentially used for dynamic city model reconstruction. View full abstract»

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  • Contextual Information-Based Multichannel Synthetic Aperture Radar Interferometry: Addressing DEM reconstruction using contextual information

    Page(s): 59 - 68
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    Interferometric synthetic aperture radar (InSAR) systems are capable of providing an estimate of the digital elevation model (DEM) of the imaged ground scene. This is usually done by means of a phase unwrapping (PU) operation. In the absence of additional regularity constraints, PU is an ill-posed problem, because the solution is not unique. Multichannel (MCh) techniques, using stacks of images of the same scene, can be used for restoring the solution uniqueness and reducing the effect of phase noise. Moreover, statistical techniques exploiting the contextual information contained in the data can provide satisfactory results. In this article, an overview of the main MCh statistical DEM reconstruction methods, developed both in the classical and in the Bayesian estimation framework, is presented. In particular, the effectiveness of the exploitation of contextual statistical models is shown by means of numerical experiments on simulated and real data sets. View full abstract»

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  • Exploiting Patch Similarity for SAR Image Processing: The nonlocal paradigm

    Page(s): 69 - 78
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    Most current synthetic aperture radar (SAR) systems offer high-resolution images featuring polarimetric, interferometric, multifrequency, multiangle, or multidate information. SAR images, however, suffer from strong fluctuations due to the speckle phenomenon inherent to coherent imagery. Hence, all derived parameters display strong signal-dependent variance, preventing the full exploitation of such a wealth of information. Even with the abundance of despeckling techniques proposed over the last three decades, there is still a pressing need for new methods that can handle this variety of SAR products and efficiently eliminate speckle without sacrificing the spatial resolution. Recently, patch-based filtering has emerged as a highly successful concept in image processing. By exploiting the redundancy between similar patches, it succeeds in suppressing most of the noise with good preservation of texture and thin structures. Extensions of patch-based methods to speckle reduction and joint exploitation of multichannel SAR images (interferometric, polarimetric, or PolInSAR data) have led to the best denoising performance in radar imaging to date. We give a comprehensive survey of patch-based nonlocal filtering of SAR images, focusing on the two main ingredients of the methods: measuring patch similarity and estimating the parameters of interest from a collection of similar patches. View full abstract»

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  • Modeling and Interpretation of Scattering Mechanisms in Polarimetric Synthetic Aperture Radar: Advances and perspectives

    Page(s): 79 - 89
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    Recent advances in scattering modeling and model-based decomposition theorem were reviewed. The notable achievements include orientation compensation processing, nonnegative eigenvalue constraint, generalized scattering models, complete information utilization, full-parameter inversion strategy, and the polarimetric-interferometric decomposition scheme. These advances contribute to make scattering models more adaptive, better fit observations and guarantee physically meaningful decomposition solutions. The key features of these advances have been summarized. Performance evaluation and further development perspectives were also discussed. One promising way is to fuse multiple data to better model scattering mechanisms, such as the polarimetric-interferometric modeling attempts. Besides, with the progress in PolSAR sensors, imaging modes (e.g., bistatic, hybrid-polarization and multi-incident-angle modes) and application requirements, the development of specific scattering mechanism interpretation techniques, multiangular decomposition, and compact/hybrid decomposition techniques are also highly preferred. View full abstract»

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  • SAR Imaging Algorithms and Some Unconventional Applications: A unified mathematical overview

    Page(s): 90 - 98
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    This article deals with two significant aspects related to synthetic aperture radar imaging (SAR-I) of relevant theoretical and applicative interest. The first objective regards the analysis of the most-used SAR-I approaches under the unified mathematical framework provided by the Porter-Bojarski integral equation. The second objective is to provide an updated overview on how SAR-I research is generalizing previous algorithms to deal with unconventional scenarios. View full abstract»

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  • The Time-Reversal Technique for SAR Focusing of Buried Targets : Theoretical improvements and practical limitations

    Page(s): 99 - 109
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    This tutorial-style article presents the novel concept of time-reversal-based airborne ground penetrating radar (TR-AGPR) and its application for soil imaging and gives a brief description of the time-reversal synthetic aperture radar SAR (TRSAR) focusing algorithm. The intent of this article is to answer the question as to whether the time-reversal based algorithm is a feasible technique for SAR focusing of a buried target (structure). View full abstract»

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  • Multipath Exploitation and Suppression for SAR Imaging of Building Interiors: An overview of recent advances

    Page(s): 110 - 119
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    Multipath that involves target scattering is an important phenomenon in synthetic aperture radar (SAR). It is highly pronounced in imaging of building interiors due to the presence of walls, ceilings, and floors surrounding the targets of interest. Multipath attributed to targets is a special type of clutter, which can be either suppressed or exploited. The latter has been the subject of many recent works in the area of SAR imaging and has led to tangible improvements in target detection and localization. In this article, we consider state-of-the-art multipath suppression and exploitation approaches, present their corresponding analytical models, and highlight their respective requirements, assumptions, and offerings. Both conventional and compressive sensing-based approaches are discussed, where the latter assumes the presence of a few behind-the-wall targets. View full abstract»

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  • Long-Wave Infrared Hyperspectral Remote Sensing of Chemical Clouds: A focus on signal processing approaches

    Page(s): 120 - 141
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    This paper focusses on the signal processing approaches necessary to achieve the three main tasks of gas-phase remote sensing: detection of a plume, identification of its constituent gases, and quantification of the amounts present. A tutorial introduction to the radiance phenomenology is given that drives the models on which exploitation algorithms are based which is followed by the fundamental aspects of the data-exploitation problem, develop algorithms that can successfully exploit data from many different sensors, and discuss the many challenges that remain open to the signal processing community. Results using real hyperspectral data sets are also presented. View full abstract»

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  • Optimal Multiuser Transmit Beamforming: A Difficult Problem with a Simple Solution Structure [Lecture Notes]

    Page(s): 142 - 148
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    Transmit beamforming is a versatile technique for signal transmission from an array of antennas to one or multiple users [1]. In wireless communications, the goal is to increase the signal power at the intended user and reduce interference to nonintended users. A high signal power is achieved by transmitting the same data signal from all antennas but with different amplitudes and phases, such that the signal components add coherently at the user. Low interference is accomplished by making the signal components add destructively at nonintended users. This corresponds mathematically to designing beamforming vectors (that describe the amplitudes and phases) to have large inner products with the vectors describing the intended channels and small inner products with nonintended user channels. View full abstract»

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  • Effective Feature Extraction and Data Reduction in Remote Sensing Using Hyperspectral Imaging [Applications Corner]

    Page(s): 149 - 154
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    With numerous and contiguous spectral bands acquired from visible light (400- 1,000 nm) to (near) infrared (1,000-1,700 nm and over), hyperspectral imaging (HSI) can potentially identify different objects by detecting minor changes in temperature, moisture, and chemical content. As a result, HSI has been widely applied in a number of application areas, including remote sensing. HSI data contains two-dimensional (2-D) spatial and one-dimensional spectral information, and naturally forms a three-dimensional (3-D) hypercube with a high spectral resolution in nanometers that enables robust discrimination of ground features. This article discusses several variations and extensions of conventional PCA to address the aforementioned challenges. These variations and extensions include slicing the HSI data for efficient computation of the covariance matrix similarly done in 2-D-PCA analysis and grouping the spectral data to preserve the local structures and further speedup the process to determine the covariance matrix. In addition, we also discuss some non-PCA-based approaches for feature extraction and data reduction, based on techniques such as band selection, random projection, singular value decomposition, and machine-learning approaches such as the support vector machine (SVM). View full abstract»

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  • Signal Processing in Visual Optics [Life Sciences]

    Page(s): 155 - 158
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    This column summarizes the recent endeavors undertaken in search of adequate characterization of the human eye's optics that involves application of various levels of signal processing tools. In particular, we focus on temporal changes in tear film and natural microfluctuations in a steady-state accommodation-the two factors that have the greatest influence on optical quality of the human eye. View full abstract»

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  • [Dates Ahead]

    Page(s): 159
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Aims & Scope

IEEE Signal Processing Magazine publishes tutorial-style articles on signal processing research and applications, as well as columns and forums on issues of interest.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Min Wu
University of Maryland, College Park
United States 

http://www/ece.umd.edu/~minwu/