BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Asia/Singapore BEGIN:DAYLIGHT DTSTART:20380119T111407 TZOFFSETFROM:+0800 TZOFFSETTO:+0800 RRULE:FREQ=YEARLY;BYDAY=3TU;BYMONTH=1 TZNAME:+08 END:DAYLIGHT BEGIN:STANDARD DTSTART:19820101T000000 TZOFFSETFROM:+0730 TZOFFSETTO:+0800 RRULE:FREQ=YEARLY;BYDAY=1FR;BYMONTH=1 TZNAME:+08 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260504T130904Z UID:225E960C-1778-49C4-8D31-38376808FAB6 DTSTART;TZID=Asia/Singapore:20260504T100000 DTEND;TZID=Asia/Singapore:20260504T110000 DESCRIPTION:Abstract:\n\nSince the introduction of phased array in 1905 by Karl Braun\, a Nobel Laureate\, array signal processing has advanced signi ficantly over the past century. The era of adaptive array was started by J ack Capon\, signified by his seminal paper in 1969. The Capon beamformer h as better resolution and much better interference rejection capability tha n the data-independent beamformer by Karl Braun\, provided that the array steering vector corresponding to the signal of interest (SOI) and the arra y covariance matrix is accurately known\, and the SOI is uncorrelated to a ll other signals impinging on the array. However\, whenever the knowledge of the SOI steering vector is imprecise\, the number of data snapshots is scarce\, or the SOI is correlated with a multipath\, which are often the c ases encountered in practice\, the performance of the Capon beamformer may become worse than that of the data-independent beamformer. For over 50 ye ars\, making the Capon beamformer robust has attracted much interest and t ens of thousands of papers on robust adaptive array processing have been p ublished in the literature. To fundamentally overcome the limitations of t he Capon family of beamformers\, iterative approaches have been introduced in the recent literature. Most notably\, the iterative adaptive approach (IAA) was published in 2010 and is shown to possess strong robustness and can work well under single snapshot and arbitrary array scenarios. We will compare the nonparametric and user parameter free IAA algorithm with othe r well-known algorithms\, including the data-independent beamformer\, the Capon beamformer\, the OMP algorithm introduced in the compressed sensing literature\, as well as the parametric MUSIC and ESPRIT algorithms.\n\nLT2 6 (SS4-B2-33)\, 50 Nanyang Ave\, Singapore\, Singapore\, Singapore\, 63979 8 LOCATION:LT26 (SS4-B2-33)\, 50 Nanyang Ave\, Singapore\, Singapore\, Singap ore\, 639798 ORGANIZER:elhxie@ntu.edu.sg SEQUENCE:19 SUMMARY:IEEE Distinguished Lecture: Over a Century of Array Signal Processi ng URL;VALUE=URI:https://events.vtools.ieee.org/m/556854 X-ALT-DESC:Description: <br /><p><strong><em>Abstract:</em></strong></p>\n< p class="MsoNormal">Since the introduction of phased array in 1905 by Karl Braun\, a Nobel Laureate\, array signal processing has advanced significa ntly over the past century. The era of adaptive array was started by Jack Capon\, signified by his seminal paper in 1969. The Capon beamformer has b etter resolution and much better interference rejection capability than th e data-independent beamformer by Karl Braun\, provided that the array stee ring vector corresponding to the signal of interest (SOI) and the array co variance matrix is accurately known\, and the SOI is uncorrelated to all o ther signals impinging on the array. However\, whenever the knowledge of t he SOI steering vector is imprecise\, the number of data snapshots is scar ce\, or the SOI is correlated with a multipath\, which are often the cases encountered in practice\, the performance of the Capon beamformer may bec ome worse than that of the data-independent beamformer. For over 50 years\ , making the Capon beamformer robust has attracted much interest and tens of thousands of papers on robust adaptive array processing have been publi shed in the literature. To fundamentally overcome the limitations of the C apon family of beamformers\, iterative approaches have been introduced in the recent literature. Most notably\, the iterative adaptive approach (IAA ) was published in 2010 and is shown to possess strong robustness and can work well under single snapshot and arbitrary array scenarios. We will com pare the nonparametric and user parameter free IAA algorithm with other we ll-known algorithms\, including the data-independent beamformer\, the Capo n beamformer\, the OMP algorithm introduced in the compressed sensing lite rature\, as well as the parametric MUSIC and ESPRIT algorithms.<strong><em >&nbsp\;</em></strong></p> END:VEVENT END:VCALENDAR