Multidisciplinary Research on Advanced, High-Speed, Adaptive Signal Processing for Radar Sensors.
Abstract
This report addresses two major components of research for high speed, spacetime adaptive processing (STAP) for radar sensors, namely (1) the development of advanced algorithms for detection and parameter estimation of weak targets in the presence of jamming and clutter, and (2) the mapping of the algorithms onto massively parallel computing architectures for high speed implementation. First, advances in detection and estimation for STAP applications are achieved using joint Gaussian statistics. A cross spectral method, an optimal technique for reduced-rank STAP, and a simultaneous CFAR detection and maximum likelihood estimation STAP algorithm for airborne radar is introduced. Secondly, this report discusses new methods for parameter estimation with symmetric alpha-stable distributions and fractional lower-order moments. A Cauchy beamformer is proposed, along with a new joint spatial and Doppler frequency, high resolution estimation technique based on eigen-decomposition of the convariance matrix. Finally, this report investigates the issue of mapping the above signal processing algorithms to scaleable, portable, parallel implementations.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 01, 1997
- Accession Number
- ADA326298
Entities
People
- Chrysostomos L. Nikias
- Irving Reed
- Viktor K. Prasanna
Organizations
- University of Southern California