Azimuth Sidelobes Suppression Using Multi-Azimuth Angle Synthetic Aperture Radar Images

Wang, Yamin; Yang, Wei; Chen, Jie; Kuang, Hao; Liu, Wei; Li, Chunsheng

doi:10.3390/s19122764

Cited by 11 publications

(11 citation statements)

References 17 publications

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“…The works in [1][2][3][4][5][6] concern several aspects of the SAR imaging. In [1], the authors propose a multi-angle SAR imaging system suited for an ultrahigh speed platform and based on multi-beamforming.…”

Section: Sar Imagingmentioning

confidence: 99%

See 1 more Smart Citation

Special Issue “Synthetic Aperture Radar (SAR) Techniques and Applications”

Bovenga

2020

Sensors

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This editorial of the special issue titled “Synthetic Aperture Radar (SAR) Techniques and Applications”, reviews the nineteen papers selected for publication. The proposed studies investigate different aspects of SAR processing including signal modelling, simulation, image analysis, as well as some examples of applications. The papers are grouped according to homogeneous subjects, then objectives and methods are summarised, and the more relevant results are commented.

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Section: Sar Imagingmentioning

confidence: 99%

“…The work in [3] concerns the processing of multi-pass squinted SAR data. The proposed algorithm combines images acquired with the same azimuth squint angle on each pass for performing 3D imaging (as in SAR tomography), and data acquired with different azimuth squint angles for refining the suppression of azimuth sidelobes.…”

Section: Sar Imagingmentioning

confidence: 99%

Special Issue “Synthetic Aperture Radar (SAR) Techniques and Applications”

Bovenga

2020

Sensors

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“…Thus, it is essential to reduce the sidelobe level (SLL) to improve the carrier-to-interference ratio (CIR) and hence the overall system capacity. The SLL can be reduced by several techniques and algorithms such as the tapered amplitude windows [11][12][13][14][15][16][17][18] and advanced evolutionary optimization techniques [19][20][21][22][23][24][25][26][27][28][29][30][31][32]. Tapered beamforming applies various amplitude feeding windows to any array geometry which effectively reduces the SLL (with some increase in the main lobe beamwidth).…”

Section: Introduction 1background and Motivationmentioning

confidence: 99%

An Efficient Adaptive and Steep-Convergent Sidelobes Simultaneous Reduction Algorithm for Massive Linear Arrays

Albagory¹,

Alraddady²

2022

Electronics

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Antenna arrays have become an essential part of most wireless communications systems. In this paper, the unwanted sidelobes in the symmetric linear array power pattern are reduced efficiently by utilizing a faster simultaneous sidelobes processing algorithm, which generates nulling sub-beams that are adapted to control and maintain steep convergence toward lower sidelobe levels. The proposed algorithm is performed using adaptive damping and heuristic factors which result in learning curve perturbations during the first few loops of the reduction process and is followed by a very steep convergence profile towards deep sidelobe levels. The numerical results show that, using the proposed adaptive sidelobes simultaneous reduction algorithm, a maximum sidelobe level of −50 dB can be achieved after only 10 iteration loops (especially for very large antenna arrays formed by 256 elements, wherein the processing time is reduced to approximately 25% of that required by the conventional fixed damping factor case). On the other hand, the generated array weights can be applied to practical linear antenna arrays under mutual coupling effects, which have shown very similar results to the radiation pattern of the isotropic antenna elements with very deep sidelobe levels and the same beamwidth.

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“…This necessitates the requirement of powerful spatial filtering techniques especially for real time communications scenarios which require rapid and adaptive manipulation of the received signals. One of the most spatial filtering applications is sidelobe levels (SLL) reduction in the radiation pattern of the array, which greatly improves the quality of the received signal especially in rich interference environments [6][7][8][9][10][11][12][13]. Almost all communication systems require SLL control including radar, sonar, satellite and even space communications [14].…”

Section: Introduction 1background and Motivationmentioning

confidence: 99%

An Efficient Fast and Convergence-Controlled Algorithm for Sidelobes Simultaneous Reduction (SSR) and Spatial Filtering

Albagory

2021

Electronics

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In this paper, an efficient sidelobe levels (SLL) reduction and spatial filtering algorithm is proposed for linear one-dimensional arrays. In this algorithm, the sidelobes are beamspace processed simultaneously based on its orientation symmetry to achieve very deep SLL at much lower processing time compared with recent techniques and is denoted by the sidelobes simultaneous reduction (SSR) algorithm. The beamwidth increase due to SLL reduction is found to be the same as that resulting from the Dolph-Chebyshev window but at considerably lower average SLL at the same interelement spacing distance. The convergence of the proposed SSR algorithm can be controlled to guarantee the achievement of the required SLL with almost steady state behavior. On the other hand, the proposed SSR algorithm has been examined for spatial selective sidelobe filtering and has shown the capability to effectively reduce any angular range of the radiation pattern effectively. In addition, the controlled convergence capability of the proposed SSR algorithm allows it to work at any interelement spacing distance, which ranges from tenths to a few wavelength distances, and still provide very low SLL.

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Azimuth Sidelobes Suppression Using Multi-Azimuth Angle Synthetic Aperture Radar Images

Cited by 11 publications

References 17 publications

Special Issue “Synthetic Aperture Radar (SAR) Techniques and Applications”

Special Issue “Synthetic Aperture Radar (SAR) Techniques and Applications”

An Efficient Adaptive and Steep-Convergent Sidelobes Simultaneous Reduction Algorithm for Massive Linear Arrays

An Efficient Fast and Convergence-Controlled Algorithm for Sidelobes Simultaneous Reduction (SSR) and Spatial Filtering

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