A Sparse SAR Imaging Method Based on Multiple Measurement Vectors Model

Ao, Dongyang; Wang, Rui; Hu, Cheng; Li, Yuanhao

doi:10.3390/rs9030297

Cited by 23 publications

(22 citation statements)

References 34 publications

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“…Additionally, as missing or gapped data can be regarded as sparse sampling data, the sparse optimization method can also be implemented on sparse SAR and Inverse SAR (ISAR) data [22][23][24][25][26]. In [22], a novel ISAR imaging algorithm with a relaxation technique is proposed to focus azimuth Nevertheless, conventional SAR imaging algorithms usually perform unsatisfactorily in focusing periodically gapped raw SAR data.…”

Section: Introductionmentioning

confidence: 99%

“…The presented method in [24] effectively achieves fundamental motion compensation, range, and cross-range scaling, as well as simultaneously achieving bistatic distortion correction for bistatic ISAR. In [25], a new sparse SAR imaging method using the multiple measurement vectors model is proposed with a reduced computation cost and enhanced image quality. The presented method in [25] utilizes the structural information and matched filter processing to implement imaging under sub-Nyquist sampling.…”

Section: Introductionmentioning

confidence: 99%

“…In [25], a new sparse SAR imaging method using the multiple measurement vectors model is proposed with a reduced computation cost and enhanced image quality. The presented method in [25] utilizes the structural information and matched filter processing to implement imaging under sub-Nyquist sampling. In [26], a novel method is proposed to focus the azimuth missing SAR raw data via segmented recovery.…”

Section: Introductionmentioning

confidence: 99%

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Image Formation of Azimuth Periodically Gapped SAR Raw Data with Complex Deconvolution

Qian

Zhu

2019

Remote Sensing

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The phenomenon of periodical gapping in Synthetic Aperture Radar (SAR), which is induced in various ways, creates challenges in focusing raw SAR data. To handle this problem, a novel method is proposed in this paper. Complex deconvolution is utilized to restore the azimuth spectrum of complete data from the gapped raw data in the proposed method. In other words, a new approach is provided by the proposed method to cope with periodically gapped raw SAR data via complex deconvolution. The proposed method provides a robust implementation of deconvolution for processing azimuth gapped raw data. The proposed method mainly consists of phase compensation and recovering the azimuth spectrum of raw data with complex deconvolution. The gapped data become sparser in the range of the Doppler domain after phase compensation. Then, it is feasible to recover the azimuth spectrum of the complete data from gapped raw data via complex deconvolution in the Doppler domain. Afterwards, the traditional SAR imaging algorithm is capable of focusing the reconstructed raw data in this paper. The effectiveness of the proposed method was validated via point target simulation and surface target simulation. Moreover, real SAR data were utilized to further demonstrate the validity of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Image Formation of Azimuth Periodically Gapped SAR Raw Data with Complex Deconvolution

Qian

Zhu

2019

Remote Sensing

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“…The motion error of FMCW CSAR can be divided into the error in the direction of motion and the radial error. Most of the papers introduce radial error, but few papers introduce the error in the direction of motion for single track FMCW CSAR in detail [7][8][9][10][11][12][13]. This paper focuses on the error in the direction of motion for FMCW CSAR to improve the imaging resolution.…”

Section: Introductionmentioning

confidence: 99%

Motion Compensation Algorithm for Single Track FMCW Csar by Parametric Sparse Representation

Song¹,

Li²,

Qu³

et al. 2019

PIER C

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In recent years, FMCW CSAR (frequency modulation continue wave circular synthetic aperture radar) is more and more widely used in military reconnaissance and sea surface target recognition. However, due to the influence of external factors, it cannot move in an ideal uniform circular trajectory, resulting in low imaging resolution. In this paper, the problem of motion errors caused by nonuniform circular motion is analyzed, and the phenomenon of range unit broadening and sidelobe increase caused by nonuniform circular motion errors is simulated. The echo model is characterized by error parameters. Based on the compressed sensing imaging algorithm, motion error parameters are estimated by parametric sparse representation. The least squares method and gradient descent method are applied to estimate motion error parameters. Simulations are conducted to show that both of the methods can reach the goal that the motion compensation is realized. The result of simulations and measurement data demonstrate that the algorithm can correct nonuniform circular motion errors better and further improve the imaging resolution.

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“…However, the computation complexity of CS-based methods limits its wide applications in SAR imaging. In [19], a novel sparse SAR imaging method using the multiple measurement vectors model is presented in order to reduce the computational cost and enhance the imaging results.…”

mentioning

confidence: 99%

Editorial for Special Issue “Radar Systems for the Societal Challenges”

Soldovieri

Persico

2017

Remote Sensing

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The special issue (SI) "Radar Systems for the Societal Challenges" is an updated survey of recent advances in radar systems, encompassing several application fields and related to the impact on society. In fact, radar technology is now pervasive in society and is crucial for tackling social and economic issues for the wellbeing of the population. In fact, in addition to the classical application fields, i.e., navigation, air traffic control, and surveillance, emerging applications are now arising, which range from diagnostics and monitoring in civil engineering and cultural heritage, to medical applications, monitoring of environmental parameters, and security in urban areas. These new applications are possible thanks to the improved capabilities of radar systems, accompanied by new advanced modeling and data processing, able to tackle electromagnetically complex scenarios.Most of the works present in this SI can be categorized according to the relevant application fields.The first batch regards the use of radar systems in monitoring for environmental protection; in this area, a recent focus is on sea monitoring and observation, using radar systems on different platforms. In this frame, the first paper from Abileah et al. [1] investigates the possibilities offered by current and future satellite radar altimetry missions for the study of inland water bodies, probing into the peculiarities of the expected radar returns and their potential usage. Two other papers show the use of X-band radar systems for sea state monitoring. The first one presents a comparison between drifter and X-band wave radar for sea surface current estimation [2], while the other aims at improving the operational capabilities of X-band radar systems by performing advanced data processing for wind parameter estimation, starting from X-band radar collected in challenging rainy conditions [3]. The last paper of this batch regards the use of a new observation modality ensured by an X-Band airborne interferometric synthetic aperture radar (SAR) system for sea surface observation [4].Radar systems are now a significant observational tool in the field of Cultural Heritage monitoring, as shown by the paper from Masini et al. [5], which presents a significant example of integration of ground penetrating radar and electrical resistivity tomography for the archaeological investigation in Kaifeng (Henan, China). In [6], the capabilities of SAR interferometry have been demonstrated for monitoring ground deformation and structural instabilities over the ancient city walls (Ming Dynasty) in Nanjing City, China.Two recent fields of application of radar systems are security and healthcare. For these fields, different radar technologies are now under development and this SI presents a good survey of these research and technological efforts.In particular, good examples of the development and adaptation of the CW Doppler radar to the human detection and activity classification in through-wall configuration and for vital signs monitoring are presented in [7,8], r...

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A Sparse SAR Imaging Method Based on Multiple Measurement Vectors Model

Cited by 23 publications

References 34 publications

Image Formation of Azimuth Periodically Gapped SAR Raw Data with Complex Deconvolution

Image Formation of Azimuth Periodically Gapped SAR Raw Data with Complex Deconvolution

Motion Compensation Algorithm for Single Track FMCW Csar by Parametric Sparse Representation

Editorial for Special Issue “Radar Systems for the Societal Challenges”

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