SAR Imaging via Modern 2-D Spectral Estimation Methods. Volume 1. Imaging Methods.
Final rept. Sep 93-Oct 94,
ENVIRONMENTAL RESEARCH INST OF MICHIGAN ANN ARBOR
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This report discusses the use of modern 2-D spectral estimation algorithms for SAR imaging, and makes two principal contributions to the field of adaptive SAR imaging. First, it is a comprehensive comparison of 2-D spectral estimation methods for SAR imaging. It provides a synopsis of the algorithms available, discusses their relative merits for SAR imaging, and illustrates their performance on simulated and collected SAR imagery. The discussion of autoregressive linear predictive techniques ARLP, including the Tufts Kumaresan variant, is somewhat more general than appears in most of the literature, in that it allows the prediction element to be varied throughout the subaperture. This generality leads to a theoretical link between ARLP and one of Pisarenkos methods. The report also provides a theoretical analysis that predicts the impact of the adaptive sidelobe reduction ASR algorithm on target to clutter ratio and provides insight into order and constraint selection. Second, this work develops multi-channel variants of three related algorithms, minimum variance method MVM, reduced rank MVM RRMVM, and ASR to estimate both reflectivity intensity and interferometric height from polarimetric displaced-aperture interferometric data. Examples illustrate that MVM and ASR both offer significant advantages over Fourier methods for estimating both scattering intensity and interferometric height, and allow empirical comparison of the accuracies of Fourier, MVM, and- ometric height estimates.
- Active and Passive Radar Detection and Equipment