scispace - formally typeset
Search or ask a question
Author

Jung Ah Choi Lee

Bio: Jung Ah Choi Lee is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Spectral density estimation & Synthetic aperture radar. The author has an hindex of 1, co-authored 1 publications receiving 21 citations.

Papers
More filters
Proceedings ArticleDOI
23 Oct 1995
TL;DR: It is shown that spatially-variant apodization is a special version of the minimum variance spectral estimator (MVSE), and that it has limitations for reconstructing real-valued extended targets.
Abstract: Sidelobe artifact is a common problem in image reconstruction from finite-extent Fourier data. Conventional shift-invariant windows applied to the Fourier data, reduce sidelobe artifacts at the expense of worsened mainlobe resolution. Stankwitz et al. (1995) have suggested spatially-variant apodization (SVA) as a means of reducing the sidelobe artifacts, while preserving the mainlobe resolution. SVA adaptively selects windows from a set of raised-cosine weighting functions. The algorithm is heuristically motivated, and is known to be effective in synthetic aperture radar (SAR) imaging. However, this technique has received only limited analysis. In this paper, we formulate SVA as a spectral estimator, and show that SVA is a special version of the minimum variance spectral estimator (MVSE). We study the properties of SVA that are inherited from MVSE. Then, we consider the application of SVA to spectral estimation and Fourier reconstruction. Although SVA is effective in SAR, we show that it has limitations for reconstructing real-valued extended targets.

23 citations


Cited by
More filters
Book
01 Jan 2005
TL;DR: 1. Basic Concepts. 2. Nonparametric Methods. 3. Parametric Methods for Rational Spectra.
Abstract: 1. Basic Concepts. 2. Nonparametric Methods. 3. Parametric Methods for Rational Spectra. 4. Parametric Methods for Line Spectra. 5. Filter Bank Methods. 6. Spatial Methods. Appendix A: Linear Algebra and Matrix Analysis Tools. Appendix B: Cramer-Rao Bound Tools. Appendix C: Model Order Selection Tools. Appendix D: Answers to Selected Exercises. Bibliography. References Grouped by Subject. Subject Index.

2,620 citations

Patent
28 Feb 2002
TL;DR: In this paper, a high-definition radar imaging system and method receives image data and adaptively processes the image the data to provide a high resolution image The imaging technique employs adaptive processing using a constrained minimum variance method to iteratively compute the high definition image The highdefinition image I is expressed in range and cross-range as I(r,c)=minωHRω, where ω is a weighting vector and R is a covariance matrix of the image data.
Abstract: A high-definition radar imaging system and method receives image data and adaptively processes the image the data to provide a high resolution image The imaging technique employs adaptive processing using a constrained minimum variance method to iteratively compute the high-definition image The high-definition image I is expressed in range and cross-range as I(r,c)=minωHRω, where ω is a weighting vector and R is a covariance matrix of the image data A solution for I(r,c) is approximated by i) forming Y=[x1 xK]T/{square root over (K)} where x1 xk are beamspace looks formed from image domain looks and with y1, y2, and y3 denoting the K×1 columns of Y; ii) computing r21=y2 Ty1 and r31=y3 Ty1, and b=r21y2+r31y3; computing γ as γ = min  ( r 21 2 + r 31 2 b T  b , β - 1 r 21 2 + r 31 2 ) ; and iii) computing I(r,c) as I(r,c)=∥y1−γb∥2

62 citations

Proceedings ArticleDOI
13 May 1996
TL;DR: The authors presented a technique based on the super-spatially variant apodization (super SVA) algorithm that can interpolate the collected data to fill the gap formed by the missing data that introduces a new potential price/performance tradeoff for the system designer with specific resolution requirements.
Abstract: Synthetic aperture radar (SAR) is a coherent imaging process that requires an uninterrupted collection of Nyquist-sampled signal data. Corrupted or missing data in the collected aperture produces artifacts and reduces the achievable resolution. For the case of corrupted data due to an improperly functioning system component or an intentional interference signal, simply nulling the corrupted part of the aperture will often improve the utility of the resulting image. However, significant artifacts will likely remain as a result of the gap introduced when the bad data is removed. The authors presented a technique based on the super-spatially variant apodization (super SVA) algorithm [Stankwitz and Kosek 1995] that can interpolate the collected data to fill the gap formed by the missing data. Super-SVB was originally designed to extrapolate SAR signal data; however, it can also be used to interpolate across gaps in the data. This technique can be extended to effectively build a large aperture from a number of closely spaced, but non-abutting, e.g. sparse, apertures. This ability to fill sparse apertures introduces a new potential price/performance tradeoff for the system designer with specific resolution requirements.

58 citations

Journal ArticleDOI
TL;DR: Spatially variant apodization is reformulated for use on synthetic aperture radar imagery with an arbitrary sampling rate, and effectively eliminates sidelobe artifacts with no loss of mainlobe resolution.
Abstract: Spatially variant apodization (SVA) is reformulated for use on synthetic aperture radar imagery with an arbitrary sampling rate. The algorithm is implemented as a spatially varying three-point finite impulse response filter, and constraints on the filter parameters are developed from physically motivated concepts. By varying the parameters of the filter, the sidelobe energy is reduced with no effective loss of resolution. The procedure produces an output comparable to that of the integer Nyquist version of SVA, and effectively eliminates sidelobe artifacts with no loss of mainlobe resolution.

55 citations

Journal ArticleDOI
TL;DR: It is shown that SVA is a version of minimum-variance spectral estimation (MVSE), and a complete development of the four types of two-dimensional SVA for image reconstruction from partial Fourier data is presented.
Abstract: Sidelobe artifacts are a common problem in image reconstruction from finite-extent Fourier data. Conventional shift-invariant windows reduce sidelobe artifacts only at the expense of worsened mainlobe resolution. Spatially variant apodization (SVA) was previously introduced as a means of reducing sidelobe artifacts, while preserving mainlobe resolution. Although the algorithm has been shown to be effective in synthetic aperture radar (SAR), it is heuristically motivated and it has received somewhat limited analysis. In this paper, we show that SVA is a version of minimum-variance spectral estimation (MVSE). We then present a complete development of the four types of two-dimensional SVA for image reconstruction from partial Fourier data. We provide simulation results for various real-valued and complex-valued targets and point out some of the limitations of SVA. Performance measures are presented to help further evaluate the effectiveness of SVA.

30 citations