Radar Imaging and Feature Extraction
Abstract
Advanced spectral estimation methods are presented for radar imaging and target feature extraction. We study problems involved in inverse synthetic aperture radar (ISAR) autofocus and imaging, synthetic aperture radar (SAR) autofocus and motion compensation, superresolution SAR image formation, three-dimensional (3-D) target feature extraction via curvilinear SAR (CLSAR), and time delay estimation. For the ISAR imaging problem, we present a parametric AUTOCLEAN (AUTOfocus via CLEAN) algorithm, and two non-parametric algorithms including an adaptive Capon and a recursive APES (Amplitude and Phase EStimation). For the problems related to SAR imaging, we propose a Semi-PARametric (SPAR) algorithm for target feature extraction and superresolution image formation, and two parametric methods, MCRELAX (Motion Compensation RELAX) and MCCLEAN (Motion Compensation CLEAN), for simultaneous target feature extraction and cross-range phase error compensation. For the 3-D target feature extraction problem, an AUTOfocus algorithm based on the RELAXation-based optimization approach (AUTORELAX) is proposed to compensate the aperture errors in CLSAR and to extract 3-D target features. For the time delay estimation problem, we first present a Weighted Fourier transform and RELAXation-based (WRELAX) approach. Then, a MODE-WRELAX (MODE (Method Of Direction Estimation) together with WRELAX) algorithm is proposed for the superresolution time delay estimation.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 15, 1999
- Accession Number
- ADA364669
Entities
People
- Jiantao Li
Organizations
- University of Florida