Improved focal underdetermined system solver method for radar coincidence imaging with model mismatch

Cao, Kaicheng; Zhou, Xiaoli; Cheng, Yongqiang; Qin, Yuliang

doi:10.1117/1.jei.26.3.033001

Cited by 8 publications

(8 citation statements)

References 16 publications

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“…The random phase errors have also been considered in [17,22], which are referred as TV-TLS and R-FOUCSS, respectively. TV-TLS and R-FOUCSS methods take the effect of the modeling errors as a perturbation to the TSSRF matrix, i.e., y = (H + E)σ + n, and they prefer to compensate the perturbation matrix E rather than directly compensate the random phase errors.…”

Section: Imaging Simulations With Random Phase Errorsmentioning

confidence: 99%

“…errors [15][16][17][18][19]. Therefore, it is necessary to take consideration of modeling errors in MSCI systems in order to obtain good imaging quality.…”

Section: Introductionmentioning

confidence: 99%

“…The random phase errors in MSCI are firstly taken into account in [17,22] along with gain errors and synchronization errors, which take the co-effect of multiple modeling errors as a perturbation to the TSSRF matrix. Thus the modeling errors are compensated by alternately reconstructing the target image and estimating the perturbation matrix.…”

Section: Introductionmentioning

confidence: 99%

“…However, these methods are effective only when the relative model error (RME) is relatively small. The relative model error is defined as RM E = 20 lg( E F / H F ) in [17], where E is the perturbation matrix, and H is the true sensing matrix.…”

Section: Introductionmentioning

confidence: 99%

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Sparse Self-Calibration for Microwave Staring Correlated Imaging With Random Phase Errors

Yuan¹,

Jiang²,

Zhang³

et al. 2020

PIER C

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Microwave Staring Correlated Imaging (MSCI) technology can obtain high-resolution images in staring imaging geometry by utilizing the temporal-spatial stochastic radiation field. In MSCI, sparse-driven approaches are commonly used to reconstruct the target images when the radiation fields are accurately calculated. However, it is challenging to compute radiation filed with high precision due to the existence of random phase errors in MSCI systems. Therefore, in this paper, a self-calibration method is proposed to handle the problem. Specifically, a two-step self-calibration framework is applied which alternately reconstructs the target image and estimates the random phase errors. In the target image reconstruction step, sparse-driven approaches are utilized, while in the random phase errors calibration step, an adaptive learning rate method is adopted. Moreover, the batch-learning strategy is utilized to reduce computation burden and obtain effective convergence performance. Numerical simulations verify the advantage of the proposed method to obtain good imaging results and improve random phase errors correction performance.

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Section: Imaging Simulations With Random Phase Errorsmentioning

confidence: 99%

“…errors [15][16][17][18][19]. Therefore, it is necessary to take consideration of modeling errors in MSCI systems in order to obtain good imaging quality.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sparse Self-Calibration for Microwave Staring Correlated Imaging With Random Phase Errors

Yuan¹,

Jiang²,

Zhang³

et al. 2020

PIER C

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show abstract

“…where λ>0 is the regularization parameter that balances the trade off between deviation term x y 2 2 F -|| | | and sparsity regularizer x 0 || || . Focal Underdetermined System Solver (FOCUSS) [22], Iterative Re-weighted Least Squares (IRLS) [23,24] and Bayesian CS (BCS) [25] are typical approaches to solve equation (4), which have rapid reconstruction speed, but are easy to fall into local optimum. [26] proposed a new algorithm called Lp-RLS, which converts…”

Section: Introductionmentioning

confidence: 99%

A re-weighted smoothed L0 -norm regularized sparse reconstructed algorithm for linear inverse problems

Wang

Xiang

et al. 2019

J. Phys. Commun.

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This paper addresses the problems of sparse signal and image recovery using compressive sensing (CS), especially in the case of Gaussian noise. The main contribution of this paper is the proposal of the regularization re-weighted Composite Sine function smoothed L 0 -norm minimization (RRCSFSL0) algorithm where the Composite Sine function (CSF), the iteratively re-weighted scheme and the regularization mechanism represent the core of an approach to the solution of the problem. Compared with other state-of-the-art functions, the CSF we proposed can better approximate the L 0 -norm and improve the reconstruction accuracy, the new re-weighted scheme we adopted can promote sparsity and speed up convergence. Moreover, the use of the regularization mechanism makes the RRCSFSL0 algorithm more robust against noise. The performance of the proposed algorithm is verified via numerical experiments in the noise environment. Furthermore, experiments and comparisons demonstrate the superiority of the RRCSFSL0 algorithm in image restoration.

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Parameter estimation for ultrasonic echo signals through improved matching pursuit and flower pollination algorithms

Chang

Huang

et al. 2022

Measurement

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Improved focal underdetermined system solver method for radar coincidence imaging with model mismatch

Cited by 8 publications

References 16 publications

Sparse Self-Calibration for Microwave Staring Correlated Imaging With Random Phase Errors

Sparse Self-Calibration for Microwave Staring Correlated Imaging With Random Phase Errors

A re-weighted smoothed L0 -norm regularized sparse reconstructed algorithm for linear inverse problems

Parameter estimation for ultrasonic echo signals through improved matching pursuit and flower pollination algorithms

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