Sidelobe reduction via adaptive FIR filtering in SAR imagery

DeGraaf, S.

doi:10.1109/83.287022

Cited by 69 publications

(22 citation statements)

References 9 publications

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“…A variety of sidelobe suppression techniques are useful for traditional radar imaging, including spectral windowing, nonlinear techniques such as spatially variant apodization (SVA [27,28]), and SVA's extensions for spectral extrapolation (Super-SVA [29], Adaptive Sidelobe Reduction [30]). The effectiveness of such techniques for CSAR imaging in the subwavelength regime is questionable.…”

Section: Sidelobe Interferencementioning

confidence: 99%

Csar Imaging of Electromagnetically Coupled Conducting Scatterers

Guido¹,

Hiatt²,

Chang³

2019

PIER M

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Chipless RFID with small, printed metal tags have been proposed as a cost-effective alternative to chip-based technologies. A potentially viable configuration is to image the patches of different shapes, sizes, and orientations within a tag with a tabletop-scale synthetic aperture radar (SAR), operating in the V or W band. Information is encoded into, e.g., polarization, resonance characteristics, and phase of the scattered signal. The effect of electromagnetic coupling and sidelobe interference between closely spaced metal patches on SAR image has not been addressed in prior studies. To be specific, we analyze 60 GHz circular SAR (CSAR) imagery of subwavelength patches separated by distances on the order of wavelength. The scattered field is calculated with the method of moments (MoM) to account for EM interaction. The field is then used to form CSAR image with the polar formatting algorithm (PFA). Significant distortion of the CSAR image is found at this scale. Sidelobe interference causes image distortion and up to 7 dB of intensity modulation with patch separation. EM coupling produces an "interaction image," an artifact that extends between the patches. The source of this effect is traced to induced currents and charges residing on the patches' inner edges. Increasing system bandwidth or changing the incidence angle has minimal effect on both classes of image artifacts, highlighting the importance of accounting for them in practical system design and subsequent information processing.

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Section: Sidelobe Interferencementioning

confidence: 99%

Csar Imaging of Electromagnetically Coupled Conducting Scatterers

Guido¹,

Hiatt²,

Chang³

2019

PIER M

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“…The conventional SAR imaging methods include the DFT or windowed DFT methods. Many parametric and nonparametric spectral estimation methods have also more recently been used for SAR imaging [3], [41], [42], [43], [44]. It has been shown in [3] that the minimum variance method (which is similar to the Capon method herein) gives good SAR images while the high resolution parametric methods advocated in some of the cited references tend to be too sensitive to model errors.…”

Section: Synthetic Aperture Radar (Sar) Imaging Examplesmentioning

confidence: 99%

High-Resolution Nonparametric Spectral Analysis: Theory and Applications

Larsson¹,

Li²,

Stoica³

2017

High-Resolution and Robust Signal Processing

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Spectral estimation can be defined as the art of recovering the frequency content in a measured signal, and is a highly relevant problem in practice. In particular, many engineering problems, ranging from synthetic aperture radar (SAR) imaging to the analysis of time-series observed in seismology or astronomy, can be cast as a spectral analysis problem. A nonparametric spectral estimator is a method that, unlike parametric methods, attempts to compute the spectral content of a signal without using any a priori information or making any explicit assumption about it. While many smoothed versions of the discrete Fourier transform can be interpreted as nonparametric methods, a body of recent work has suggested more advanced such methods that are based on adaptive filterbanks. This chapter provides a review of some of the existing work in the area of nonparametric spectral estimation, including fast implementations of the most successful estimators as well as various extensions to spectral analysis of incomplete data. Numerical examples are provided to exemplify the various algorithms.

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“…This approach has been used to model both clutter [14] and objects that consist of collections of point reflectors [7] (i.e., trihedrals or corner reflectors).…”

Section: Multiresolution Process Statisticsmentioning

confidence: 99%

Multiresolution detection of coherent radar targets

Subotic

Thelen

Gorman

et al. 1997

IEEE Trans. on Image Process.

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We develop and investigate several novel multiresolution algorithms for detecting coherent radar targets embedded in clutter. These multiresolution detectors exploit the fact that prominent target scatterers interfere in a characteristic manner as resolution is changed, while multiresolution clutter signatures are random. We show, both on simulated and collected synthetic aperture radar data, that these multiresolution algorithms yield significant detection improvements over single-pixel, single-resolution constant false alarm rate (CFAR) methods that use only the finest available resolution.

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Sidelobe reduction via adaptive FIR filtering in SAR imagery

Cited by 69 publications

References 9 publications

Csar Imaging of Electromagnetically Coupled Conducting Scatterers

Csar Imaging of Electromagnetically Coupled Conducting Scatterers

High-Resolution Nonparametric Spectral Analysis: Theory and Applications

Multiresolution detection of coherent radar targets

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