Superresolution for scanning antenna

Dropkin, H.; Ly, Canh

doi:10.1109/nrc.1997.588326

Cited by 33 publications

(11 citation statements)

References 4 publications

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“…It is easy to prove that R is not the Toeplitz matrix any more, which directs the proposed fast algorithms to be invalid [14]- [21]. However, as (9) shows, it is a band matrix. On the basis of the research on the existing matrix inverse methods [22], [23], we compute R −1 with the D&C method.…”

Section: Conquermentioning

confidence: 99%

Divide and conquer: A fast matrix inverse method of iterative adaptive approach for real beam superresolution

Zhang¹,

Li²,

Huang³

et al. 2014

2014 IEEE Geoscience and Remote Sensing Symposium

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This paper present an efficient matrix inverse algorithm of the recent iterative adaptive approach (IAA) in the application of real beam superresolution (RBS). Based on the inherently band structure of the covariance matrix, the computational complexity of inverse can be reduced by avoiding the computation of zero elements. To achieve this, the divide and conquer (D&C) method will be introduced to fast inverse the covariance matrix. Numerical simulations illustrate the efficiency of the proposed algorithm.

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Section: Conquermentioning

confidence: 99%

Divide and conquer: A fast matrix inverse method of iterative adaptive approach for real beam superresolution

Zhang¹,

Li²,

Huang³

et al. 2014

2014 IEEE Geoscience and Remote Sensing Symposium

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“…In mathematics, research shows that the azimuth echo of real-aperture radar can be modeled as the convolution of the antenna pattern and target scattering distribution [ 8 , 9 , 10 ]. Therefore, current studies are devoted to improving radar azimuth resolution by deconvolution methods, such as Wiener filtering [ 11 ], Tikhonov regularization (TREGU) [ 12 ], truncate singular value decomposition (TSVD) [ 13 ], the Richardson–Lucy (RL) method [ 14 , 15 ], iterative adaptive approach (IAA) [ 16 , 17 ] and so on.…”

Section: Introductionmentioning

confidence: 99%

A Superfast Super-Resolution Method for Radar Forward-Looking Imaging

Huo

Zhang

et al. 2021

Sensors

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The super-resolution method has been widely used for improving azimuth resolution for radar forward-looking imaging. Typically, it can be achieved by solving an undifferentiable L1 regularization problem. The split Bregman algorithm (SBA) is a great tool for solving this undifferentiable problem. However, its real-time imaging ability is limited to matrix inversion and iterations. Although previous studies have used the special structure of the coefficient matrix to reduce the computational complexity of each iteration, the real-time performance is still limited due to the need for hundreds of iterations. In this paper, a superfast SBA (SFSBA) is proposed to overcome this shortcoming. Firstly, the super-resolution problem is transmitted into an L1 regularization problem in the framework of regularization. Then, the proposed SFSBA is used to solve the nondifferentiable L1 regularization problem. Different from the traditional SBA, the proposed SFSBA utilizes the low displacement rank features of Toplitz matrix, along with the Gohberg-Semencul (GS) representation to realize fast inversion of the coefficient matrix, reducing the computational complexity of each iteration from O(N3) to O(N2). It uses a two-order vector extrapolation strategy to reduce the number of iterations. The convergence speed is increased by about 8 times. Finally, the simulation and real data processing results demonstrate that the proposed SFSBA can effectively improve the azimuth resolution of radar forward-looking imaging, and its performance is only slightly lower compared to traditional SBA. The hardware test shows that the computational efficiency of the proposed SFSBA is much higher than that of other traditional super-resolution methods, which would meet the real-time requirements in practice.

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“…One simple and efficient approach for forward-looking imaging is to use scanning radar to acquire the real beam echo firstly. Then, super-resolution techniques are conducted to improve the azimuth resolution [6]. In [7], the real iterative adaptive approach (Real-IAA) was introduced to realize superresolution for forward-looking scanning radar, which can improve the azimuth resolution efficiently without introducing artificial ringing.…”

Section: Introductionmentioning

confidence: 99%

A `Divide and Conquer' Regularization Imaging Method for Forward-Looking Scanning Radar Azimuth Super-Resolution

Tan¹,

Li²,

Huang³

et al. 2018

PIER M

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Sparse regularization imaging method (SRIM) is an effective approach to implement azimuth super-resolution for forward-looking scanning radar. However, for the scene that contains adjacent strong targets in the continuous weak background, SRIM may destroy the structure of the scene when trying to separate the closely located targets. In this paper, a divide and conquer regularization imaging method (DC-RIM) is proposed to solve this problem. Firstly, the data are divided into two channels by the mean-variance segmentation method. Normally, we consider that the data of channel I contain strong scatterers and that the data of channel II contain weak background. Afterwards, SRIM is conducted on channel I to distinguish the targets. For the data of channel II, a region enhancement regularization method is particularly proposed to acquire a good structure of the scene by making use of two-order gradient information of the data. Finally, a good imaging result can be obtained by combining the results of two channels. Experiments based on both synthetic and real data are given to verify the effectiveness of the method.

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Superresolution for scanning antenna

Cited by 33 publications

References 4 publications

Divide and conquer: A fast matrix inverse method of iterative adaptive approach for real beam superresolution

Divide and conquer: A fast matrix inverse method of iterative adaptive approach for real beam superresolution

A Superfast Super-Resolution Method for Radar Forward-Looking Imaging

A `Divide and Conquer' Regularization Imaging Method for Forward-Looking Scanning Radar Azimuth Super-Resolution

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