SAR autofocusing viewed as adaptive beamforming on prominent scatterers

Yadin, E.

doi:10.1109/nrc.1994.328114

Cited by 12 publications

(11 citation statements)

References 2 publications

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“…Subaperture processing [16] can be applied to the developed approaches to reduce the computational load. Consider a special case where each subaperture consists of two pulses and all the subapertures are connected with one pulse.…”

Section: Relation To Other Autofocus Methodsmentioning

confidence: 99%

“…For a complex target that does not have a stable prominent scatterer, an estimate of the pulse-to-pulse phase difference of the reference point can be made by taking the phase differences for each range cell and averaging them weighted by the amplitudes of the content of each range cell [5] . An alternative is to average the phase differences in the range cells where only strong scatterers exist [6][7] [16] . However phase unwrapping is crucial for these approaches because phase averaging is needed for estimating the phase of the translational motion.…”

Section: Introductionmentioning

confidence: 99%

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Autofocus for inverse synthetic aperture radar (ISAR) imaging

2001

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Autofocus is a key step of inverse synthetic aperture radar (ISAR) imaging. In this paper four new approaches to autofocussing based on the application of beamforming and subspace concepts to ISAR imaging are developed. Their relations to maximum likelihood (ML) estimation are identified. A common feature of these techniques is the estimation of the complex vector formed by the exponential function of phase rather than phase itself so that phase unwrapping is obviated. The Cramer Rao lower bound (CRLB) of the estimated complex vector corresponding to translational motion and the CRLB of the estimated distance between two scatterers are derived. The results of processing simulated and real data confirm the validity of proposed approaches. AbstractAutofocus is a key step of inverse synthetic aperture radar (ISAR) imaging. In this paper four new approaches to autofocussing based on the application of beamforming and subspace concepts to ISAR imaging are developed. Their relations to maximum likelihood (ML) estimation are identified. A common feature of these techniques is the estimation of the complex vector formed by the exponential function of phase rather than phase itself so that phase unwrapping is obviated. The Cramer-Rao lower bound (CRLB) of the estimated complex vector corresponding to translational motion and the CRLB of the estimated distance between two scatterers are derived. The results of processing simulated and real data confirm the validity of proposed approaches.

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Section: Relation To Other Autofocus Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Autofocus for inverse synthetic aperture radar (ISAR) imaging

2001

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“…Step 6: Selecting responses of only high-quality scatterers: A technique to select quality scatterers is to look at the contrast measure of the scatterer's phase history [7,20]. The contrast measure is defined as…”

Section: Pga and Its Improvementsmentioning

confidence: 99%

“…The simplicity image entropy (20) in the development, let g stands for g(n, k;dct). Thus the corresponding image entropy is…”

Section: Appendixmentioning

confidence: 99%

Improved phase gradient autofocus algorithm based on segments of variable lengths and minimum‐entropy phase correction

Azouz

2015

IET Radar, Sonar & Navigation

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Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) is an essential tool for modern remote sensing applications. Owing to its size and weight constraints, UAV is very sensitive to atmospheric turbulence that causes serious trajectory deviations. In this study, an improved phase gradient autofocus (PGA) motion compensation approach is proposed for UAV-SAR imagery. The approach is implemented in two steps. The first step determines the length of each segment depending on number of good quality scatterers and motion errors obtained from navigation data. In the second step, a novel minimum-entropy phase correction based on the discrete cosine transform (DCT) coefficients is proposed. In this approach, transform phase error estimated by PGA to DCT-coefficients that represent the phase error in the frequency or time-frequency domain. The entropy of a focused image is utilised as the optimisation function of the DCT-coefficients to improve the final image quality. Finally, real-data experiments show that the proposed approach is appropriate for highly precise imaging of UAV-SAR equipped with only low-accuracy inertial navigation systems.

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“…Step 1: Select one range bin automatically according to the criteria in [5], while the selection is typically an interactive process in original PPP.…”

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confidence: 99%

Autofocusing of SAR images using STFRFT-based preprocessing

Tao¹,

Liu²,

Zhao³

2012

Electronics Letters

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Prominent point processing (PPP) is a very popular synthetic aperture radar (SAR) autofocus algorithm, which can estimate errors caused by any type of unknown motion or other error source and has high accuracy under the assumption that a prominent point scatterer is available within an imaged scene. Proposed is an advanced PPP algorithm with a preprocessing technique based on the short-time fractional Fourier transform (STFRFT) to relax the limitation on the scene content. The performance of the proposed autofocus algorithm is validated using real radar data.Introduction: Autofocusing is one of the key steps to gather wellfocused images for airborne synthetic aperture radar (SAR) [1]. Many SAR autofocus algorithms have been developed and used successfully. There exist three basic types of autofocus algorithm [2]: subaperturebased autofocus (SBA) (e.g. map drift), optimisation-based autofocus (OBA) (e.g. contrast optimisation) and point-based autofocus (PBA). The SBA and OBA techniques are mostly suitable for images with low-order phase errors. The well-known PBA technique is prominent point processing (PPP) [1]. This method operates on the received signal before azimuth compression and provides direct measurement of the phase history from a prominent point scatterer. The PPP algorithm has been proved to be very accurate and reliable, because we do not assume any specific model of the phase error function. However, the PPP needs a prominent point to obtain properly focused images. Moreover, if the signal from a prominent point is not dominant relative to the summation of same-range targets, the phase history will not provide an accurate reference phase (see Figs. 1a and b). This Letter presents an improved PPP approach with a preprocessing technique based on the short-time fractional Fourier transform (STFRFT). Unlike the original PPP method which needs a very high signal-to-background ratio of the prominent point, the proposed autofocus approach is an efficient improvement, which can enhance the ratio by filtering in the short-time fractional Fourier domain (STFRFD), and it becomes robust against limitation on the scene content.

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SAR autofocusing viewed as adaptive beamforming on prominent scatterers

Cited by 12 publications

References 2 publications

Autofocus for inverse synthetic aperture radar (ISAR) imaging

Autofocus for inverse synthetic aperture radar (ISAR) imaging

Improved phase gradient autofocus algorithm based on segments of variable lengths and minimum‐entropy phase correction

Autofocusing of SAR images using STFRFT-based preprocessing

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