BEGIN:VCALENDAR
VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:US/Pacific
BEGIN:DAYLIGHT
DTSTART:20190310T030000
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3
TZNAME:PDT
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:20191103T010000
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11
TZNAME:PST
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20190313T235701Z
UID:A09B0E2D-E29F-466C-BA2E-2F3F508F1F12
DTSTART;TZID=US/Pacific:20190329T123000
DTEND;TZID=US/Pacific:20190329T133000
DESCRIPTION:Radar not only has found widespread application in advanced dri
 ver assistance systems (ADAS) but also is one of the key technologies to e
 nable environmental perception for autonomous driving under all kinds of w
 eather conditions. Today\, a typical self-driving car has been equipped wi
 th more than 10 radars\, enabling a radar-based 360 degree surround sensin
 g. The radar sensors with high resolution and multi-functionality are high
 ly demanded for autonomous driving. As compared to traditional phased-arra
 y radar system with the same number of transmit and receive antennas\, mul
 tiple-input multiple-output (MIMO) radar achieves significantly improved s
 patial resolution by exploiting waveform diversity. MIMO radar technology 
 has been receiving considerable attention in designing millimeter-wave rad
 ar sensors. In the talk\, we will review the fundamentals of MIMO radar\, 
 highlighting the features that make this technology a good fit for automot
 ive radars which are required to be high resolution. Topics will be discus
 sed in this talk include radar waveform orthogonality\, sparse array desig
 n\, high resolution angle finding with compressed sensing\, imaging radar\
 , interference mitigation\, radar machine learning and cybersecurity in co
 nnected and autonomous vehicles.\n\nCo-sponsored by: IEEE Systems Council 
 Chapter\n\nSpeaker(s): Dr. Sun\, \n\nRoom: 424\, Bldg: VEC\, 1250 Bellflow
 er Blvd\, Long Beach\, California\, United States\, 90840
LOCATION:Room: 424\, Bldg: VEC\, 1250 Bellflower Blvd\, Long Beach\, Califo
 rnia\, United States\, 90840
ORGANIZER:sean.kwon@csulb.edu
SEQUENCE:0
SUMMARY:The Advantages and Challenges of MIMO Radar for Autonomous Driving
URL;VALUE=URI:https://events.vtools.ieee.org/m/195149
X-ALT-DESC:Description: <br /><p class="p1"><span class="s1">Radar not only
  has found widespread application in advanced driver assistance systems (A
 DAS) but also is one of the key technologies to enable environmental perce
 ption for autonomous driving under all kinds of weather conditions. Today\
 , a typical self-driving car has been equipped with more than 10 radars\, 
 enabling a radar-based 360 degree surround sensing. The radar sensors with
  high resolution and multi-functionality are highly demanded for autonomou
 s driving. As compared to traditional phased-array radar system with the s
 ame number of transmit and receive antennas\, multiple-input multiple-outp
 ut (MIMO) radar achieves significantly improved spatial resolution by expl
 oiting waveform diversity. MIMO radar technology has been receiving consid
 erable attention in designing millimeter-wave radar sensors. In the talk\,
  we will review the fundamentals of MIMO radar\, highlighting the features
  that make this technology a good fit for automotive radars which are requ
 ired to be high resolution. Topics will be discussed in this talk include 
 radar waveform orthogonality\, sparse array design\, high resolution angle
  finding with compressed sensing\, imaging radar\, interference mitigation
 \, radar machine learning and cybersecurity in<span class="Apple-converted
 -space">&nbsp\; </span>connected and autonomous vehicles.</span></p>
END:VEVENT
END:VCALENDAR