A Fast Eigenvector-Based Autofocus Method for Sparse Aperture ISAR Sensors Imaging of Moving Target

Liu, Qiuchen; Wang, Yong

doi:10.1109/jsen.2018.2880899

Cited by 19 publications

(5 citation statements)

References 29 publications

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“…The most common phase gradient autofocus (PGA) [40,41] does not assume any specific model of the phase error function and estimates phase errors from echoes reflected from multiple strong scatterers. The method has several variations such as the eigenvector method [39] and its fast computation counterparts [42]. Alternatively, a few approaches consider minimizing the image entropy to obtain a sharp image.…”

Section: Wideband Autofocusingmentioning

confidence: 99%

An Overview of Advances in Signal Processing Techniques for Classical and Quantum Wideband Synthetic Apertures

Vouras¹,

Mishra²,

Artusio‐Glimpse³

et al. 2022

Preprint

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Rapid developments in synthetic aperture (SA) systems, which generate a larger aperture with greater angular resolution than is inherently possible from the physical dimensions of a single sensor alone, are leading to novel research avenues in several signal processing applications. The SAs may either use a mechanical positioner to move an antenna through space or deploy a distributed network of sensors. With the advent of new hardware technologies, the SAs tend to be denser nowadays. The recent opening of higher frequency bands has led to wide SA bandwidths. In general, new techniques and setups are required to harness the potential of wide SAs in space and bandwidth. Herein, we provide a brief overview of emerging signal processing trends in such spatially and spectrally wideband SA systems. This guide is intended to aid newcomers in navigating the most critical issues in SA analysis and further supports the development of new theories in the field. In particular, we cover the theoretical framework and practical underpinnings of wideband SA radar, channel sounding, sonar, radiometry, and optical applications. Apart from the classical SA applications, we also discuss the quantum electric-field-sensing probes in SAs that are currently undergoing active research but remain at nascent stages of development.

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Section: Wideband Autofocusingmentioning

confidence: 99%

An Overview of Advances in Signal Processing Techniques for Classical and Quantum Wideband Synthetic Apertures

Vouras¹,

Mishra²,

Artusio‐Glimpse³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Echo data may be discontinuous with sparse aperture 5 – 8 The coherence between pulses and echo data is destroyed with sparse aperture, which affects the autofocus imaging performance 9 – 11 Serious side lobes and energy leakage are induced by the range-Doppler (RD) imaging algorithm 2 .…”

Section: Introductionmentioning

confidence: 99%

Inverse synthetic aperture radar autofocus imaging of block structure targets with sparse aperture

Zhu

Zhang

Guo³

et al. 2023

J. Appl. Rem. Sens.

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Inverse synthetic aperture radar (ISAR) autofocus imaging performance is challenged by the residual phase errors that arise from the traditional motion compensation methods with sparse aperture. Moreover, sparse reconstruction algorithms usually ignore the block sparsity in the ISAR image, which cannot recover structured information of the block structure target completely. An imaging method based on joint minimum Tsallis entropy and pattern-coupled sparse Bayesian learning algorithm is proposed to achieve ISAR autofocus imaging of block structure targets with sparse aperture. A pattern-coupled hierarchical complex Gaussian prior is utilized to characterize the correlation among ISAR image pixels in the complex domain. Such a prior model has the potential to encourage block-sparse patterns and achieve ISAR sparse aperture imaging of block structure targets without knowing the block partition prior information. The residual phase errors are estimated based on the minimum Tsallis entropy criterion during the sparse reconstruction of ISAR image to achieve ISAR autofocus imaging, which has the advantage of improving the computational efficiency compared with minimum Shannon entropy criterion. The superiority of the proposed algorithm is verified by the experimental results based on both simulated and measured data.

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“…Inverse synthetic aperture radar (ISAR) has the capacity to obtain two-dimensional images of the targets and is widely used in military and civilian areas [1,2]. Traditional range Doppler (RD) ISAR technique shows that the high-range resolution can be obtained by adopting a wideband signal, and the relative angle variation between the radar platform and target is a guarantee of high azimuth resolution [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Fast, super‐resolution sparse inverse synthetic aperture radar imaging via continuous compressive sensing

Lv¹,

Ma²,

Ma³

et al. 2022

IET Signal Processing

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Conventional inverse synthetic aperture radar (ISAR) imaging with sparse aperture usually suffers from high side lobes and wide main lobes, which limit the applications of radar super‐resolution imaging, multi‐target resolution, and cognitive reconfiguration. This paper proposes a fast, super‐resolution imaging method employing continuous compressive sensing for sparse‐aperture ISAR. First, the received echo in each range bin is characterised as a linear combination of multiple frequencies shown in a continuous atomic set, established into an atomic norm minimisation (ANM) mode. Second, to improve the resolution and reduce the computational burden significantly, a locally convergent iterative algorithm based on the alternating direction method of multipliers, which iteratively performs ANM with a sound reweighting strategy, is implemented. Then, the low‐rank Toeplitz covariance matrix, which contains the information of the target, is obtained. Subsequently, the Vandermonde decomposition of the Toeplitz covariance matrix is performed to acquire the locations and intensities of the scattering points. Finally, the super‐resolution result is generated by depicting the estimated scatterers in the image. Extensive numerical experiments demonstrate that the proposal is highly effective in recovering the super‐resolution image and shows better performance than state‐of‐the‐art methods.

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A Fast Eigenvector-Based Autofocus Method for Sparse Aperture ISAR Sensors Imaging of Moving Target

Cited by 19 publications

References 29 publications

An Overview of Advances in Signal Processing Techniques for Classical and Quantum Wideband Synthetic Apertures

An Overview of Advances in Signal Processing Techniques for Classical and Quantum Wideband Synthetic Apertures

Inverse synthetic aperture radar autofocus imaging of block structure targets with sparse aperture

Fast, super‐resolution sparse inverse synthetic aperture radar imaging via continuous compressive sensing

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