Poster 3.6
Novel Signal Processing Architectures for Knowledge-based STAP Algorithms

Matthew C French - USC / ISI, Jinwoo Suh - USC / ISI, Stephen P Crago - USC / ISI, John Damoulakis - USC / ISI

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

Abstract

New algorithms are being developed in the radar community that blend a priori knowledge source processing with traditional digital signal processing concepts. This operational blend necessitates a system-level architecture capable of delivering both high processing throughput and memory bandwidth. This paper derives these system parameters from the Knowledge Aided Pre-Whitening algorithm and evaluates the performance of two High Performance Embedded Computing architectures, the Imagine and Raw processors, on these kernels. The implementation results are compared with the measured performance of a conventional system based on the PowerPC with Altivec. The results show these processors exhibit significant improvements over conventional systems and that each architecture has its own strengths and weaknesses.

Bio

Matthew French is a Project Leader at the University of Southern California's Information Sciences Institute in Arlington, Virginia. His area of research is in tools to support application mapping to novel computing architectures and rugged processing environments. He is currently Co-Principal Investigator of the Abstract Machines for Polymorphous computing (AMP) project, funded by the DARPA IPTO Polymorphous Computing Architectures (PCA) program and Principal Investigator of the NASA funded Reconfigurable Hardware IN Orbit (RHINO) project. Mr. French received a MEE and BS from Cornell University in 1996 specializing in digital signal processing.