Learn the latest about waveform design and optimization from ASU alumnus Brian O’Donnell at this SenSIP Seminar Series event.
Fast Gradient Descent For Multi-Objective Waveform Design
Presented by Brian O’Donnell, Research Engineer, Sensor and E&M Applications Laboratory, Georgia Tech Research Institute
Thursday, November 29, 2016
Engineering Research Center (ERC) 189, Tempe campus [map]
Refreshments will be served
Gradient descent can be used to design waveforms with a diverse set of desirable properties. Mathematical simplifications in their derivations reduce the computational cost from O(N^2) to O(N logN). This presentation will discuss waveform properties which can be optimized including autocorrelation, cross correlation and spectral weighting. Gradients can be derived for unimodular and amplitude modulated waveforms, and for biphase and polyphase sequences as well. Furthermore, gradients can be combined to optimize multiple criteria simultaneously. The results shown will include how sequences with autocorrelation and cross correlation constraints meet the known bounds, and how including spectral constraints affects autocorrelation and cross correlation levels.
Brian O’Donnell is a Research Engineer in the Sensor and Electromagnetic Applications Laboratory at Georgia Tech Research Institute. Brian received his bachelor’s in Electrical and Computer Engineering from Worcester Polytechnic Institute, his master’s in Electrical Engineering from the University of New Hampshire, and his doctorate in Electrical Engineering from Arizona State University. His research interests include waveform design, synthetic aperture signal processing, and simulation of complex acoustic environments.
This seminar series is sponsored by the SenSIP Center and NSF I/UCRC, with technical co-sponsorship by the IEEE Signal Processing and Communications Chapter, Phoenix Section.