BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20240310T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20231105T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20231224T210135Z UID:3FEA1D5E-D776-4B3B-9CAB-11AD59429821 DTSTART;TZID=America/New_York:20231212T121500 DTEND;TZID=America/New_York:20231212T131500 DESCRIPTION:The problem of imaging of objects within or through multilayere d dielectric media appears in many areas\, including those in ground-penet rating radar (GPR) imaging\, through-the-wall radar imaging (TWRI)\, intra -wall and subsurface imaging\, and medical imaging. These general areas co ver many important defense and civilian applications such as those in coun terterrorism and law enforcement operations\, firefighting\, earthquake re scue missions\, detection of buried subsurface objects and minerals in GPR \, millimeter wave imaging of concealed weapons and contraband carried by personnel\, to name a few. In many situations\, however\, the dielectric m edia induce shadowing effects on targets\, resulting in image degradation and errors in geo-locating or\, possibly\, complete masking of targets. Fu rthermore\, in most practical situations the imaging of targets should be done in real-time\, requiring the development of fast data acquisition sch emes as well as highly efficient microwave imaging techniques that can ful ly account for wave propagation through various dielectric layers or walls .\n\nIn this lecture\, a comprehensive overview of various image reconstru ction techniques for objects in stratified media will be given for both SA R-based and multiple-input multiple-output (MIMO) based systems\, and for both real-time imaging and sparsity-based imaging scenarios. For the forme r\, we will describe the use of efficient 2D and 3D Diffraction Tomography (DT) techniques which use first order Born approximation together with su ccessive implementations of spatial fast-Fourier transform (FFT) and inver se-FFT (IFFT)\, to arrive at high-resolution images. Such fast-imaging tec hniques\, however\, do not address the problem posed by long data acquisit ion time associated with most microwave-imaging scenarios. To address this problem\, assuming a sparse target space\, one can resort to the use of C ompressive Sensing (CS) to significantly reduce the number of antennas and /or collected frequency points. In our implementation of CS\, the wall or multilayered media effects are accurately and efficiently accounted for in the sparse-image reconstruction through the use of approximate expression s for the Green’s functions of multi-layered lossy dielectric medium. In particular\, the use of total variation minimization (TVM) and its advant ages over the l1-norm minimization\, which is often used in the standard r adar implementation of CS\, will be detailed. Numerical and experimental r esults for DT-based and CS-based radar imaging in various GPR and TWRI sce narios will be given in the presentation.\n\nSpeaker(s): Prof. Ahmad Hoorf ar\, \n\nBldg: Pavillon Principal\, B 600.16\, la Galerie Rolland\, 2500 C hem. de Polytechnique\, Montréal\, Quebec\, Canada\, H3T 1J4 LOCATION:Bldg: Pavillon Principal\, B 600.16\, la Galerie Rolland\, 2500 Ch em. de Polytechnique\, Montréal\, Quebec\, Canada\, H3T 1J4 ORGANIZER:elham.baladi@polymtl.ca SEQUENCE:11 SUMMARY:Real Time and Sparse Reconstructed Radar Imaging Through Stratified Media URL;VALUE=URI:https://events.vtools.ieee.org/m/381919 X-ALT-DESC:Description: <br /><p>The problem of imaging of objects within o r through multilayered dielectric media appears in many areas\, including those in ground-penetrating radar (GPR) imaging\, through-the-wall radar i maging (TWRI)\, intra-wall and subsurface imaging\, and medical imaging. T hese general areas cover many important defense and civilian applications such as those in counterterrorism and law enforcement operations\, firefig hting\, earthquake rescue missions\, detection of buried subsurface object s and minerals in GPR\, millimeter wave imaging of concealed weapons and c ontraband carried by personnel\, to name a few. In many situations\, howev er\, the dielectric media induce shadowing effects on targets\, resulting in image degradation and errors in geo-locating or\, possibly\, complete m asking of targets. Furthermore\, in most practical situations the imaging of targets should be done in real-time\, requiring the development of fast data acquisition schemes as well as highly efficient microwave imaging te chniques that can fully account for wave propagation through various diele ctric layers or walls.</p>\n<p>&nbsp\;</p>\n<p>In this lecture\, a compreh ensive overview of various image reconstruction techniques for objects in stratified media will be given for both SAR-based and multiple-input multi ple-output (MIMO) based systems\, and for both real-time imaging and spars ity-based imaging scenarios. For the former\, we will describe the use of efficient 2D and 3D Diffraction Tomography (DT) techniques which use first order Born approximation together with successive implementations of spat ial fast-Fourier transform (FFT) and inverse-FFT (IFFT)\, to arrive at hig h-resolution images. Such fast-imaging techniques\, however\, do not addre ss the problem posed by long data acquisition time associated with most mi crowave-imaging scenarios. To address this problem\, assuming a sparse tar get space\, one can resort to the use of Compressive Sensing (CS) to signi ficantly reduce the number of antennas and/or collected frequency points.& nbsp\; In our implementation of CS\, the wall or multilayered media effect s are accurately and efficiently accounted for in the sparse-image reconst ruction through the use of approximate expressions for the Green&rsquo\;s functions of multi-layered lossy dielectric medium. In particular\, the us e of total variation minimization (TVM) and its advantages over the l1-nor m minimization\, which is often used in the standard radar implementation of CS\, will be detailed. Numerical and experimental results for DT-based and CS-based radar imaging in various GPR and TWRI scenarios will be given in the presentation.</p> END:VEVENT END:VCALENDAR