Fractional sparse energy representation method for ISAR imaging

Zhao, Zhijie; Tao, Ran; Li, Gang; Wang, Yue

doi:10.1049/iet-rsn.2017.0550

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…To improve the azimuth resolution of ISAR images a fractional sparse energy representation method combined with fractional Fourier transform is proposed in [26]. The clock jitter influence on the signal to noise ratio (SNR) of an analog-to-digital-converter of the ISAR signal acquired from the space object is analyzed in [27].…”

Section: Synthetic and Inverse Synthetic Aperture Radar Issuesmentioning

confidence: 99%

Pulsar Emissions, Signal Modeling and Passive ISAR Imaging

Lazarov

2019

Sensors

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The present work addresses pulsar Crab Nebula emissions from point of view of their modeling and applications for asteroid detection and imaging by applying inverse synthetic aperture radar (ISAR) principles. A huge value of the plasma’s effective temperature is a reason for pulsar emission coherency, a property of great practical meaning for a space objects navigation, localization and imaging. Based on measurement data obtained by Goldstone-Apple Valley and Arecibo radio telescopes, an original time frequency grid mathematical model of pulsar emissions is created. Passive ISAR scenario, a space object’s geometry and a model of pulsar signals reflected from the space object’s surface are also described and graphically illustrated. A new range compression approach for ISAR imaging is suggested and demonstrated. In order to reduce the level of additive white Gaussian noise in signals and enlarge the signal to noise ratio in the final image, coherent summation of multiple complex images is applied. To prove the correctness of the geometry, signal models and theoretical analysis, results of numerical experiments are provided.

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Section: Synthetic and Inverse Synthetic Aperture Radar Issuesmentioning

confidence: 99%

Pulsar Emissions, Signal Modeling and Passive ISAR Imaging

Lazarov

2019

Sensors

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“…FRT is a fundamental transform that has important applications in several fields including signal processing, optics, and wave propagation [67]. The applications of FRT in signal processing include time-frequency analysis [69], [72], filter design [73]- [76], image processing [77]- [79], video processing [79], beamforming [80], [81], pattern recognition [82], phase retrieval [83], optical information processing [64], [82], sonar signal processing [84], inverse synthetic-aperture radar (ISAR) imaging [85] among numerous others. FRT provides extra degrees of freedom when transforming signals into intermediate time-frequency domains while keeping the ordinary FT as a special case.…”

Section: Introductionmentioning

confidence: 99%

Joint Time-Vertex Fractional Fourier Transform

Kartal¹,

Özgünay²,

Koç³

2022

Preprint

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Graphs signal processing successfully captures highdimensional data on non-Euclidean domains by using graph signals defined on graph vertices. However, data sources on each vertex can also continually provide time-series signals such that graph signals on each vertex are now time-series signals.Joint time-vertex Fourier transform (JFT) and the associated framework of time-vertex signal processing enable us to study such signals defined on joint time-vertex domains by providing spectral analysis. Just as the fractional Fourier transform (FRT) generalizes the ordinary Fourier transform (FT), we propose the joint time-vertex fractional Fourier transform (JFRT) as a generalization to the JFT. JFRT provides an additional fractional analysis tool for joint time-vertex processing by extending both temporal and vertex domain Fourier analysis to fractional orders. We theoretically show that the proposed JFRT generalizes the JFT and satisfies the properties of index additivity, reversibility, reduction to identity, and unitarity (for certain graph topologies). We provide theoretical derivations for JFRT-based denoising as well as computational cost analysis. Results of numerical experiments are also presented to demonstrate the benefits of JFRT.

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“…FRT is of importance for signal processing [6][7][8][9][10][11][12][13], time/space-frequency representations [5,[14][15][16], image processing [17][18][19][20], video processing [21,22], pattern recognition [23], radar/sonar signal processing [24,25] and beamforming [26,27]. FRT finds applications in wave and beam propagations, diffraction and generally in Fourier optics [1,28,29].…”

Section: Introductionmentioning

confidence: 99%

Operator theory-based discrete fractional Fourier transform

Koç

2019

SIViP

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The fractional Fourier transform is of importance in several areas of signal processing with many applications including optical signal processing. Deploying it in practical applications requires discrete implementations, and therefore defining a discrete fractional Fourier transform (DFRT) is of considerable interest. We propose an operator theory-based approach to defining the DFRT. By deploying hyperdifferential operators, a DFRT matrix can be defined compatible with the theory of the discrete Fourier transform. The proposed DFRT only uses the ordinary Fourier transform and the coordinate multiplication and differentiation operations. We also propose and compare several alternative discrete definitions of coordinate multiplication and differentiation operations, each of which leads to an alternative DFRT definition. Unitarity and approximation to the continuous transform properties are also investigated in detail. The proposed DFRT is highly accurate in approximating the continuous transform.

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Fractional sparse energy representation method for ISAR imaging

Cited by 7 publications

References 29 publications

Pulsar Emissions, Signal Modeling and Passive ISAR Imaging

Pulsar Emissions, Signal Modeling and Passive ISAR Imaging

Joint Time-Vertex Fractional Fourier Transform

Operator theory-based discrete fractional Fourier transform

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