Algorithms for Optimal Processing of Polarimetric Radar Data
Abstract
This report describes algorithms that make optimal use of polarimetric radar information to detect and classify targets in a ground clutter background. The optimal polarimetric detector (OPD) is derived; this algorithm processes the complete polarization scattering matrix (PSM) and provides the best possible detection performance from polarimetric radar data. Also derived is the best linear polarimetric detector, the polarimetric matched filter (PMF); the structure of this detector is shown to be related to simple polarimetric target types. Finally, the polarimetric whitening filter (PWF) is derived; this constant false alarm rate (CFAR) detector provides a simple alternative to the OPD for detecting targets in clutter. New K-distributed polarimetric target and clutter models are described; these models are used to predict the performance of the OPD, the PMF, and the PWF. The performance of these three algorithms is compared with that of simpler detectors that use only amplitude information to detect targets. The ability to classify target types by exploiting differences in polarimetric scattering properties is also discussed.
Document Details
- Document Type
- Technical Report
- Publication Date
- Nov 06, 1989
- Accession Number
- ADA217330
Entities
People
- Leslie M. Novak
- Michael B. Sechtin
- Michael C. Burl
Organizations
- Massachusetts Institute of Technology