BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Australia/Adelaide BEGIN:DAYLIGHT DTSTART:20241006T030000 TZOFFSETFROM:+0930 TZOFFSETTO:+1030 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=10 TZNAME:ACDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20250406T020000 TZOFFSETFROM:+1030 TZOFFSETTO:+0930 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=4 TZNAME:ACST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20241125T075220Z UID:CC0AE28E-8D66-4C8E-90EB-F5AB349F01BC DTSTART;TZID=Australia/Adelaide:20241122T173000 DTEND;TZID=Australia/Adelaide:20241122T183000 DESCRIPTION:Dear IEEE members and guests\,\n\nThe next IEEE Control\, Aeros pace and Electronic Systems (CAES) seminar will be on Friday\, 22nd Novemb er\, 2022 at 5:30 pm (Adelaide time).\n\nThe speaker is Prof Puneet Singla \, he will be presenting a seminar on:\n\nInformation and Resource Managem ent (INFORM) For Accurate Tracking of Resident Space Objects\n\nSpace situ ation awareness (SSA)\, including space surveillance and characterization of all space objects and environments\, is critical for national and econo mic security. SSA is the ability to detect\, track and characterize passiv e and active space objects. In light of the large number of Resident Space Objects (RSOs)\, and the generally accepted notion that our knowledge abo ut the number and nature of most of the objects is severely limited\, an u nmet and urgent need exists for accurate tracking and characterization of RSOs. A common example involves assigning probabilities of collision of be tween two different RSOs. For RSO tracking\, the core information needed i s the orbit parameters and their associated uncertainties specified at a g iven epoch. This allows for accurate forward prediction but owing to both the nonlinearity of the orbital dynamics and measurement sparsity\, the un certainty associated with RSOs orbit increases in time. Given the fact tha t none of the prior accidental collision between tracked objects was obser ved in real time as they occurred\, underscores the need for SSA.\n\nThis talk will focus on recent development of mathematical and computational ap proaches for accurate tracking of RSOs within the geostationary (GEO) regi me as well as beyond GEO (XGEO). The crux of the work lies in accounting f or uncertainties in orbit and sensor models\, characterizing the evolution of the uncertainty of the RSO position\, and integrating disparate source s of sensor data with the model output using a Bayesian framework. The pro bability density function associated with state uncertainty is utilized to compute effective information metrics that reflect the information gain a ssociated with ground-based observation platforms. These data driven metri cs can be used to pose an optimization problem that provides an optimal se nsor schedule to yield useful observations of high valued targets in space . To accommodate the increasing number of sensors and manage the computati onal challenges associated with the model data fusion process\, it is nece ssary to develop a computational engine that gracefully scales with the re solution of the desired solution. By accurately characterizing the uncerta inty associated with both process and measurement models\, this work offer s systematic design of low-complexity model-data fusion or filtering algor ithms with significant improvement in nominal performance and computationa l effort. Results from studies corresponding to tracking RSOs\, where trad itional methods either fail or perform very poorly\, are considered to ass ess the reliability and limitations of the newly established methods.\n\nF inally\, some results corresponding to application of this framework to ot her aerospace applications such as reachability analysis for air mobility and surveillance of an area of interest with autonomous agents such as Unm anned Air Vehicles (UAVs) equipped with various sensors will be discussed. \n\nSpeaker(s): Puneet Singla\n\nRoom: N132\, Bldg: Engineering North\, Th e University of Adelaide\, Adelaide\, South Australia\, Australia\, 5000\, Virtual: https://events.vtools.ieee.org/m/444239 LOCATION:Room: N132\, Bldg: Engineering North\, The University of Adelaide\ , Adelaide\, South Australia\, Australia\, 5000\, Virtual: https://events. vtools.ieee.org/m/444239 ORGANIZER:waashwal@ieee.org SEQUENCE:26 SUMMARY:Information and Resource Management (INFORM) For Accurate Tracking of Resident Space Objects URL;VALUE=URI:https://events.vtools.ieee.org/m/444239 X-ALT-DESC:Description: <br /><p>Dear IEEE members and guests\,</p>\n<p>The next IEEE Control\, Aerospace and Electronic Systems (CAES) seminar will be on Friday\, 22nd November\, 2022 at 5:30 pm (Adelaide time).</p>\n<p>Th e speaker is Prof Puneet Singla\, he will be presenting a seminar on:</p>\ n<p><strong>Information and Resource Management (INFORM) For Accurate Trac king of Resident Space Objects</strong></p>\n<p>Space situation awareness (SSA)\, including space surveillance and characterization of all space obj ects and environments\, is critical for national and economic security. SS A is the ability to detect\, track and characterize passive and active spa ce objects. In light of the large number of Resident Space Objects (RSOs)\ , and the generally accepted notion that our knowledge about the number an d nature of most of the objects is severely limited\, an unmet and urgent need exists for accurate tracking and characterization of RSOs. A common e xample involves assigning probabilities of collision of between two differ ent RSOs. For RSO tracking\, the core information needed is the orbit para meters and their associated uncertainties specified at a given epoch. This allows for accurate forward prediction but owing to both the nonlinearity of the orbital dynamics and measurement sparsity\, the uncertainty associ ated with RSOs orbit increases in time. Given the fact that none of the pr ior accidental collision between tracked objects was observed in real time as they occurred\, underscores the need for SSA.</p>\n<p>This talk will f ocus on recent development of mathematical and computational approaches fo r accurate tracking of RSOs within the geostationary (GEO) regime as well as beyond GEO (XGEO). The crux of the work lies in accounting for uncertai nties in orbit and sensor models\, characterizing the evolution of the unc ertainty of the RSO position\, and integrating disparate sources of sensor data with the model output using a Bayesian framework. The probability de nsity function associated with state uncertainty is utilized to compute ef fective information metrics that reflect the information gain associated w ith ground-based observation platforms. These data driven metrics can be u sed to pose an optimization problem that provides an optimal sensor schedu le to yield useful observations of high valued targets in space. To accomm odate the increasing number of sensors and manage the computational challe nges associated with the model data fusion process\, it is necessary to de velop a computational engine that gracefully scales with the resolution of the desired solution. By accurately characterizing the uncertainty associ ated with both process and measurement models\, this work offers systemati c design of low-complexity model-data fusion or filtering algorithms with significant improvement in nominal performance and computational effort. R esults from studies corresponding to tracking RSOs\, where traditional met hods either fail or perform very poorly\, are considered to assess the rel iability and limitations of the newly established methods.</p>\n<p>Finally \, some results corresponding to application of this framework to other ae rospace applications such as reachability analysis for air mobility and su rveillance of an area of interest with autonomous agents such as Unmanned Air Vehicles (UAVs) equipped with various sensors will be discussed.</p> END:VEVENT END:VCALENDAR