Dimensionality Reduction for Sensor Arrays
Next-generation RF arrays will have the ability to generate data at tremendous rates. In this talk we will discuss how this data deluge can be managed using dimensionality reduction at the array. We will start by giving an overview of past approaches of dimensionality reduction, including classical beamforming and compressed sensing, and the trade-offs inherent in adaptivity and performance.
We then discuss how array snapshots of broadband signals can be (provably) embedded in a low dimensional subspace without loss of array gain. One consequence of this model is a new approach to broadband beamforming, which is both computationally efficient and outperforms classical methods while being highly flexible in regards to array geometry and signal bandwidth. A second consequence is a new technique for dimensionality reduction that is built directly into the analog-to-digital conversion and can dramatically reduce the hardware requirements for broadband beamforming. Finally, we will discuss how these dimensionality reduction techniques can be adapted to changing environmental conditions.
Dr. Romberg is also presenting another talk at 1pm Minimax Problems in Reinforcement Learning
Date and Time
Location
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- Date: 13 Dec 2023
- Time: 06:00 PM to 07:30 PM
- All times are (UTC-05:00) Eastern Time (US & Canada)
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- Syracuse University
- 111 College Pl
- Syracuse, New York
- United States 13210
- Building: Center of Science & Technology
- Room Number: CST 4-201
- Click here for Map
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Contact Sam Stone stone@ieee.org
- Starts 26 November 2023 11:04 AM
- Ends 13 December 2023 11:00 AM
- All times are (UTC-05:00) Eastern Time (US & Canada)
- No Admission Charge
Speakers
Justin Romberg of Georgia Institute of Technology
Biography:
Dr. Justin Romberg is the Schlumberger Professor in the School of Electrical and Computer Engineering and Associate Director of the Center for Machine Learning at the Georgia Institute of Technology, where he has been on the faculty since 2006. Dr. Romberg received the B.S.E.E. (1997), M.S. (1999) and Ph.D. (2004) degrees from Rice University in Houston, Texas. From Fall 2003 until Fall 2006, he was a Postdoctoral Scholar in Applied and Computational Mathematics at the California Institute of Technology. In 2008 he received an ONR Young Investigator Award, in 2009 he received a PECASE award and a Packard Fellowship, and in 2010 he was named a Rice University Outstanding Young Engineering Alumnus, and in 2021 he received the IEEE Jack S. Kilby Signal Processing Medal. He is a Fellow of the IEEE.
Broadly speaking, Dr. Romberg’s research interests are in the intersection of signal processing, optimization, and machine learning. One of his current interests is how online and distributed algorithms developed for statistical learning and inference can be used for next-generation sensor arrays.
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