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Proceedings Paper

Nonparametric missing sample spectral analysis and its applications to interrupted SAR
Author(s): Duc Vu; Luzhou Xu; Jian Li
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Paper Abstract

We consider nonparametric adaptive spectral analysis of complex-valued data sequences with missing samples occurring in arbitrary patterns. We first present two high-resolution missing-data spectral estimation algorithms: the Iterative Adaptive Approach (IAA) and the Sparse Learning via Iterative Minimization (SLIM) method. Both algorithms can significantly improve the spectral estimation performance, including enhanced resolution and reduced sidelobe levels. Moreover, we consider fast implementations of these algorithms using the Conjugate Gradient (CG) technique and the Gohberg-Semencul-type (GS) formula. Our proposed implementations fully exploit the structure of the steering matrices and maximize the usage of the Fast Fourier Transform (FFT), resulting in much lower computational complexities as well as much reduced memory requirements. The effectiveness of the adaptive spectral estimation algorithms is demonstrated via several 2-D interrupted synthetic aperture radar (SAR) imaging examples.

Paper Details

Date Published: 4 May 2011
PDF: 14 pages
Proc. SPIE 8051, Algorithms for Synthetic Aperture Radar Imagery XVIII, 80510J (4 May 2011); doi: 10.1117/12.886639
Show Author Affiliations
Duc Vu, Univ. of Florida (United States)
Luzhou Xu, Univ. of Florida (United States)
Jian Li, Univ. of Florida (United States)

Published in SPIE Proceedings Vol. 8051:
Algorithms for Synthetic Aperture Radar Imagery XVIII
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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