High-definition vector imaging for synthetic aperture radar

Benitz, Gerald R.

doi:10.1109/acssc.1997.679095

Cited by 47 publications

(13 citation statements)

References 10 publications

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“…The spectral splatter problem of Fourier processing remains. Another is "high-definition vector imaging" by Benitz [10], which presents the spectral analysis problem as that of probing a field of view with a narrow antenna beam. The method attempts to overcome the spectral splatter problem by applying an adaptive nulling technique to suppress targets in the sidelobes of the beam.…”

Section: Introductionmentioning

confidence: 99%

Two-Dimensional ESPRIT With Tracking for Radar Imaging and Feature Extraction

Burrows

2004

IEEE Trans. Antennas Propagat.

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ESPRIT processing appears to be the best of the known spectral-analysis techniques. It provides the highest resolution and has no spectral splatter. By applying matrix eigenstructure analysis, it gives a direct answer to the direct question "What frequencies, real or complex, are present in the data and what are their amplitudes?" Conventional Fourier techniques, as well as some of the other higher-resolution methods, answer the less direct question "What amplitudes, applied to a set of regularly-spaced real frequencies, best represent the data?" Then comes the problem of interpreting the amplitudes.These attributes of ESPRIT, in the two-dimensional version described here, make it a natural for radar signal processing, where it answers the need for high-resolution imaging, free of sidelobes in range and Doppler, and for high-fidelity target feature extraction. For example, the uncertainty in the scatterering-center locations in an ESPRIT image extracted from high-quality static-range radar data collected over a bandwidth of 1 GHz is just a few millimeters; for conventional Fourier processing of the same data the uncertainty is many centimeters. The signature of the base edge of a perfectly conducting cone extracted from static-range data by ESPRIT agrees accurately with the signature predicted by edgediffraction theory.This report starts with a mathematical model for the radar data, describes a technique for "resampling" the data to achieve a more perfect fit with the ESPRIT data model, summarizes the twodimensional ESPRIT algorithm itself, and presents examples of its performance. The appendix covers the details of this least-mean-square version of ESPRIT, including an enhancement that allows the scatterers to be tracked individually. The algorithm properly distinguishes between target locations having one coordinate in common, and it automatically associates correctly in pairs each entry in the list of ranges with the corresponding entry in the list of range rates. 1The Fourier and ESPRIT X-band radar images of an aluminum round-nosed cone target at nose-on aspect derived from measured static-range data of 1 GHz bandwidth. The target's rotation axis and the radar's magnetic-field polarization on transmit and receive were vertical (HH polarization); the target's body-axis of symmetry and the radar line of sight were horizontal. 2 2 The complex diffraction coefficient of the base edge extracted by ESPRIT processing, from the same measured static-range HH-polarization data used for Figure 1, compared with the coefficient predicted by the geometric theory of diffraction. 3 3 ESPRIT X-band images from different aspects and 360-composite images, both in B&W and, to include amplitude information, in false color. The images were extracted from the same measured static-range X-band HH-polarization radar data used for the previous figures. 9 4 Narrow-angle imaging with ESPRIT, including zoom views showing the smaller location uncertainty that greater signal-to-noise ratio provides. 11 5 360° trajectories of t...

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Section: Introductionmentioning

confidence: 99%

Two-Dimensional ESPRIT With Tracking for Radar Imaging and Feature Extraction

Burrows

2004

IEEE Trans. Antennas Propagat.

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“…Additionally, the relative heights of the cylinder and dihedral indicate Table 9: Single-primitive model order, confusion, and pose statistics that the dihedral response will be broader than the cylinder response [47]; this accounts in part for the better detection of the dihedral. 14 Of note in Table 9 is the excellent type classification performance of the algorithm: in almost every trial in which the primitive is detected, its type is correctly identified. This suggests that the limited type information provided by the even-bounce/odd-bounce discriminator in the data extraction stage, as discussed in Section 8.2.2, is not an impediment to type estimation.…”

Section: Single-primitive Targetsmentioning

confidence: 97%

“…There has recently been a considerable amount of work [14,15,16,17] studying the fundamental behavior of anisotropic scatterers in SAR for the purpose of target recognition. With the exception of the approach by McClure and Carin [16], these limit consideration to isolated atomic scatterers, and in the case of McClure and Carin's approach, they treat the scattering behavior of multiple scatterers as a simple linear superposition of those scatterers.…”

Section: Subaperture Analysismentioning

confidence: 99%

A Unified Multiresolution Framework for Automatic Target Recognition (ATR)

Fisher¹,

Grimson²,

Willsky³

2001

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“…In addition, the use of overlapping subaperture images in SAR has been a fundamental component of adaptive SAR processing techniques. [44][45][46] Furthermore, subaperture analysis is an essential ingredient of techniques developed for fast backprojection image formation for SAR. For example, sub-apertures can be used for fast backprojection techniques.…”

Section: Introductionmentioning

confidence: 99%

Insights into the complicated SAR signature shapes induced by braking targets

Garren

2018

Algorithms for Synthetic Aperture Radar Imagery XXV

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This investigation considers the shapes of synthetic aperture radar (SAR) imagery signature smears that are caused by surface targets that perform braking maneuvers during the SAR collection time. It is known that such maneuvering target signatures can have a wide variety of two-dimensional (2D) shapes, as opposed to the simpler parabolic signatures that are induced by constant velocity targets. The current paper examines the theoretical properties of these 2D signature shapes for cases in which the specific parameters of the target braking maneuver temporal profile are varied, including the rate at which the target decreases speed, the total amount of speed change, and the speed transition time within the SAR collection interval. Furthermore, the current investigation yields new insights regarding the complicated SAR signature shapes that are indicative of targets undergoing such braking maneuvers. This analysis reveals that the SAR signature for a given braking target is effectively a composite of three curved smear portions. One portion is a part of a parabola that is obtained from the constant-velocity target motion at the initial SAR collection time. Next, the second portion is that of a part of a different parabola that is generated from the final constant target velocity segment during the SAR collection interval. The third curved portion of the full moving target signature forms a connection between the parts of the two parabolas that are due to the initial and final constant velocity segments of the full target motion during the SAR measurement interval.

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High-definition vector imaging for synthetic aperture radar

Cited by 47 publications

References 10 publications

Two-Dimensional ESPRIT With Tracking for Radar Imaging and Feature Extraction

Two-Dimensional ESPRIT With Tracking for Radar Imaging and Feature Extraction

A Unified Multiresolution Framework for Automatic Target Recognition (ATR)

Insights into the complicated SAR signature shapes induced by braking targets

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