Poster 3.7
STAP training through knowledge-aided predictive modeling

Nathan A Goodman - ECE Dept., University of Arizona, Prashanth R Gurram - ECE Dept, University of Arizona

Wed, 28 April 2004, 9:30 AM - 10:20 AM

Abstract

In this paper, we investigate a spectral-domain approach to estimating the interference covariance matrix used in space-time adaptive processing. Traditionally, an estimate of the interference covariance matrix is obtained by averaging the space-time covariance matrices of multiple range bins. Unfortunately, the spectral content of these data snapshots usually varies, which corrupts the covariance estimate for the desired range. We propose to use knowledge sources to identify angle-Doppler spectral regions having the same underlying scattering statistics. Then, we use real-time data to form a synthetic aperture radar image, which is inherently an estimate of non-moving ground clutter. We then average the SAR pixels within each homogeneous region. The resulting clutter power map is used, along with knowledge of the radar system and scenario geometry, to compute the interference covariance matrix. Using simulated data, we demonstrate the potential performance of such a technique, demonstrate its dependence on accurate space-time steering vectors, and provide an example of using data to compensate for imperfect knowledge.

Bio

Nathan A. Goodman received the B.S., M.S., and Ph.D. degrees in electrical engineering from the University of Kansas in 1995, 1997, and 2002, respectively. He is currently an Assistant Professor with the Department of Electrical and Computer Engineering at the University of Arizona. Within the department, Dr. Goodman directs the Laboratory for Sensor and Array Signal Processing. From 1996 to 1998, Dr. Goodman was an RF Systems Engineer for Texas Instruments, Dallas, TX, and from 1998 to 2002, he was a Graduate Research Assistant in the Radar Systems and Remote Sensing Laboratory at the University of Kansas. His research interests are in radar, sonar, and array signal processing. Dr. Goodman was awarded the Madison A. and Lila Self Graduate Fellowship upon returning to the University of Kansas in 1998. He was also awarded the IEEE 2001 International Geoscience and Remote Sensing Symposium Interactive Session Prize Paper Award.