ECE Seminar Series

Electrical and Computer Engineering Seminar

Title: Passive Radar Detection and Equivariant Cohomology
Speaker: Dr. Stephen Howard
Affiliation: Australian Defence Science and Technology Organisation (DSTO)
Day: Tuesday, April 14, 2015
Time: 2:00 pm - 3:00 pm
Location: LSC 308-310

Abstract: Target detection for a multi-static passive radar, in which the radar uses multiple illuminators of opportunity, as well as multiple target surveillance receivers, is considered. It is shown that this detection problem can be formulated as a statistical test of whether a signal of known rank is present in both the reference and target surveillance channels, or only in the reference channels, in the presence of independent gaussian white noise across all channels. Bayesian and generalized likelihood ratio tests for target detection are derived for both known and unknown receiver noise variances. The Bayesian detectors involve integrals over the manifolds of subspaces (Grassmannians). It is shown that results from Equivariant Cohomology theory can be used to exactly compute these integrals leading to exact Bayesian detectors.

Bio: S. D. Howard graduated in 1982 from LaTrobe University, Melbourne, Australia. He received his M.S. and Ph.D. degrees in mathematics from La Trobe University in 1984 and 1990, respectively. He joined the Australian Defence Science and Technology Organisation (DSTO) in 1991, where he has been involed in research in the area electronic surveillance and radar systems. He has led the DSTO research effort into the development of algorithms in all areas of electronic surveillance, including radar pulse train deinterleaving, precision radar parameter estimation and tracking, estimation of radar intrapulse modulation, and advanced geolocation techniques. Since 2003, he has led the DSTO long-range research program in radar resource management, waveform design and passive radar detection. He was the recipient of a three year DSTO Fellowship for research the area of "Information Geometry and Compressive Sensing for Sensors and Sensor Networks"