BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20261101T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20261016T233832Z UID:76A5F7D7-BE78-4762-8EF3-2974D6027B19 DTSTART;TZID=America/New_York:20261016T113000 DTEND;TZID=America/New_York:20261016T123000 DESCRIPTION:A challenging problem in radar imaging is the localization and tracking of moving targets with high resolution. Imaging modalities such a s synthetic aperture radar use the relative motion of the antenna with res pect to a stationary scene to generate high-resolution imagery via convent ional Fourier inversion techniques applied over a coherent processing inte rval. The presence of moving targets and dynamics in the scene of interest violates the core assumptions of synthetic aperture formation\, introduci ng major artifacts and potential limitations in the resolution of syntheti c aperture imaging systems. Motion in the scene introduces spatially varyi ng\, velocity-dependent range variations which must be corrected for accur ate parameter estimation\, image refocusing\, and tracking of key targets of interest. Furthermore\, conventional radar systems are designed to opti mize resolution in range\, and to exhibit Doppler tolerance for optimal ta rget detection in lieu of high Doppler/velocity resolution\, where the two correspond to a fundamental trade-off in waveform design under the classi cal formulations.\n\nIn this talk\, we will discuss novel approaches towar ds super-resolution in ground moving target imaging (GMTI). We will first introduce a convex optimization-based approach that leverages a natural lo w-rank+sparse decomposition on the phase-space reflectivity domain\, which characterizes location and velocity parameters for the targets in the sce ne in a lifted\, higher dimensional parameter space to admit a structured linear inverse problem. We analyze the capabilities and limitations of thi s method\, which performs simultaneous stationary background recovery alon g with the recovery of ground moving targets. We next motivate a novel sup er-resolution technique for GMTI based on the atomic norm minimization (AN M) framework that reformulates the off-grid recovery of sparse moving targ et parameters as a semi-definite program that is tractable per slow-time/a ntenna-location at sub-resolution cell precision levels. To this end\, we use ANM to decompose the received SAR data into the echoes received from e ach scatterer in the scene\, which facilitates processing echoes individua lly across the slow-time samples. We then use the estimates from the ANM d ecomposition for simultaneous localization and 2D velocity estimation by f ormulating a quadratic inverse problem\, which is solved by a Gauss-Newton method. We derive new and novel trade-offs between SAR imaging parameters and the number of targets resolved under the separation conditions of ANM theory\, and establish the conditions required on the antenna trajectory for the performance guarantees of our method. We finally demonstrate the o ff-grid\, sub-resolution cell localization and 2D velocity estimation capa bility of our SAR-GMTI method via numerical simulations\, with comparisons to grid-based approaches and robustness evaluations in the presence of ad ditive noise\, clutter\, and trajectory errors. We finally discuss future directions towards extensions to problems such as passive source localizat ion\, and towards handling low signal-to-clutter ratio conditions.\n\nSpea ker(s): Dr. Yonel\, \n\nAgenda: \n11:00 A.M.-11:30 A.M.: Arrival\, Check-I n\, Get a Plate of Food\n\n11:30 A.M.-12:30 P.M.: IEEE Technical Lecture b y Dr. Bariscan Yonel\n\n12:30 P.M.-1:00 P.M.: (Optional) Additional Time f or Questions & Answers\, Depart\n\nRoom: KL 253\, Bldg: Kettering Labs (KL )\, University of Dayton (UD)\, 435 Stonemill Rd\, University Park (Dayton )\, Ohio\, United States\, 45409-2546\, Virtual: https://events.vtools.iee e.org/m/581807 LOCATION:Room: KL 253\, Bldg: Kettering Labs (KL)\, University of Dayton (U D)\, 435 Stonemill Rd\, University Park (Dayton)\, Ohio\, United States\, 45409-2546\, Virtual: https://events.vtools.ieee.org/m/581807 ORGANIZER:michael.callahan.10@us.af.mil SEQUENCE:79 SUMMARY:Super-Resolution Ground Moving Target Imaging via Atomic Norm Minim ization URL;VALUE=URI:https://events.vtools.ieee.org/m/581807 X-ALT-DESC:Description: <br /><p class="MsoNormal"><span lang="EN" style="f ont-size: 12pt\; line-height: 115%\;">A challenging problem in radar imagi ng is the localization and tracking of moving targets with high resolution . Imaging modalities such as synthetic aperture radar use the relative mot ion of the antenna with respect to a stationary scene to generate high-res olution imagery via conventional Fourier inversion techniques applied over a coherent processing interval. The presence of moving targets and dynami cs in the scene of interest violates the core assumptions of synthetic ape rture formation\, introducing major artifacts and potential limitations in the resolution of synthetic aperture imaging systems. Motion in the scene introduces spatially varying\, velocity-dependent range variations which must be corrected for accurate parameter estimation\, image refocusing\, a nd tracking of key targets of interest. Furthermore\, conventional radar s ystems are designed to optimize resolution in range\, and to exhibit Doppl er tolerance for optimal target detection in lieu of high Doppler/velocity resolution\, where the two correspond to a fundamental trade-off in wavef orm design under the classical formulations.&nbsp\;</span></p>\n<p class=" MsoNormal"><span lang="EN" style="font-size: 12pt\; line-height: 115%\;">I n this talk\, we will discuss novel approaches towards super-resolution in ground moving target imaging (GMTI). We will first introduce a convex opt imization-based approach that leverages a natural low-rank+sparse decompos ition on the phase-space reflectivity domain\, which characterizes locatio n and velocity parameters for the targets in the scene in a lifted\, highe r dimensional parameter space to admit a structured linear inverse problem . We analyze the capabilities and limitations of this method\, which perfo rms simultaneous stationary background recovery along with the recovery of ground moving targets. We next motivate a novel super-resolution techniqu e for GMTI based on the atomic norm minimization (ANM) framework that refo rmulates the off-grid recovery of sparse moving target parameters as a sem i-definite program that is tractable per slow-time/antenna-location at sub -resolution cell precision levels. To this end\, we use ANM to decompose t he received SAR data into the echoes received from each scatterer in the s cene\, which facilitates processing echoes individually across the slow-ti me samples. We then use the estimates from the ANM decomposition for simul taneous localization and 2D velocity estimation by formulating a quadratic inverse problem\, which is solved by a Gauss-Newton method. We derive new and novel trade-offs between SAR imaging parameters and the number of tar gets resolved under the separation conditions of ANM theory\, and establis h the conditions required on the antenna trajectory for the performance gu arantees of our method. We finally demonstrate the off-grid\, sub-resoluti on cell localization and 2D velocity estimation capability of our SAR-GMTI method via numerical simulations\, with comparisons to grid-based approac hes and robustness evaluations in the presence of additive noise\, clutter \, and trajectory errors. We finally discuss future directions towards ext ensions to problems such as passive source localization\, and towards hand ling low signal-to-clutter ratio conditions.</span></p><br /><br />Agenda: <br /><p>11:00 A.M.-11:30 A.M.: Arrival\, Check-In\, Get a Plate of Food< /p>\n<p>11:30 A.M.-12:30 P.M.: IEEE Technical Lecture by Dr. Bariscan Yone l</p>\n<p>12:30 P.M.-1:00 P.M.: (Optional) Additional Time for Questions & amp\; Answers\, Depart</p>\n<p>&nbsp\;</p> END:VEVENT END:VCALENDAR