Student Paper 1.10
STAP Detection Using Space-Time Autoregressive Filtering
John A. Russ - Brigham Young University, David W. Casbeer - Electrical Engineering BYU, A. Lee Swindlehurst - Brigham Young University
Tue, 27 April 2004, 9:30 AM - 10:20 AM
Application of Space-Time Adaptive Processing (STAP) in real situations requires dimension-reducing methods. This is due to both the large computational cost involved in calculating the interference statistics and the smaller number of stationary training samples available to estimate the clutter covariance. Recently, auto-regressive (AR) filtering techniques have been used to help reduce computation and secondary sample support requirements in STAP scenarios. In this paper, we compare the detection performance of several AR-based algorithms with more standard GLRT-type approaches. In particular, we consider the Parametric Amplitude Matched Filter (PAMF) and the Space-Time Auto-Regressive Filter (STAR), and show that they outperform standard GLRT tests, especially in challenging situations with low sample support. Among the parametric methods considered, the STAR approach provides the most robust overall performance.
David W. Casbeer - Electrical Engineering BYU
DAVID CASBEER received the B.S. degree in electrical engineering in 2003 from Brigham Young University where he is currently working toward a M.S. degree.
Prof. A. Lee Swindlehurst - Brigham Young University
A. LEE SWINDLEHURST received the B.S., summa cum laude, and M.S. degrees in Electrical Engineering from Brigham Young University, Provo, Utah, in 1985 and 1986, respectively, and the PhD degree in Electrical Engineering from Stanford University in 1991.
From 1983 to 1984 he was employed with Eyring Research Institute of Provo, UT, as a scientific programmer. During 1984-1986, he was a Research Assistant in the Department of Electrical Engineering and Brigham Young University, working on various problems in signal processing and estimation theory. He was awarded an Office of Naval Research Graduate Fellowship for 1985-1988, and during most of that time was affiliated with the Information Systems Laboratory at Stanford University. From 1986-1990, he was also employed at ESL, Inc., of Sunnyvale, CA, where he was involved in the design of algorithms and architectures for several radar and sonar signal processing systems. He joined the faculty of the Department of Electrical and Computer Engineering at Brigham Young University in 1990, where he holds the position of Full Professor and is currently serving as department chair. During 1996-1997, he held a joint appointment as a visiting scholar at both Uppsala University, Uppsala, Sweden, and at the Royal Institute of Technology, Stockholm, Sweden. His research interests include sensor array signal processing for radar and wireless communications, detection and estimation theory, and system identification, and he has over 125 publications in these areas.
Dr. Swindlehurst is a Fellow of the IEEE, is currently serving as Secretary of the IEEE Signal Processing Society, as a member of the Sensor Array and Multichannel Signal Processing Technical Committee in the same society, as a member of the Editorial Board for the EURASIP Journal on Wireless Communications and Networking and is a past Associate Editor for the IEEE Transactions on Signal Processing. He was the Technical Program Chair for the 1998 IEEE Digital Signal Processing Workshop and for the 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. He is also a recipient of the 2000 IEEE W. R. G. Baker Prize Paper Award (?Maximum Likelihood Methods in Radar Array Signal Processing,? IEEE Proceedings, February, 1998, co-author: Petre Stoica), and is co-author of a paper that received the IEEE Signal Processing Society Young Author Best Paper Award in 2001 (?Spatial Signature Estimation for Uniform Linear Arrays with Unknown Gains and Phases,? IEEE Transactions on Signal Processing, August, 1999, co-authors: David Astely and Bjorn Ottersten).