Fast compressed sensing SAR imaging using stepped frequency waveform

Li, Bo; Liu, Falin; Zhou, Chongbin; Lv, Yue; Hu, Jingqiu

doi:10.1109/icmmt.2016.7761827

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…The top subfigures show the 1D phase error added to the raw data. The middle subfigures show the results of approximated observation-based CS-SAR imaging (named as CS-Omega-K [16,17]) without phase error correction. The bottom subfigures show the results of Algorithm 1.…”

Section: Simulation and Experimental Resultsmentioning

confidence: 99%

“…Based on Equation (27), we acquire the approximated observation-based CS-SAR model (named as CS-Omega-K [16,17]) as follow:

\min_{G} {{‖ boldnormal S - normalI (G) ‖}_{F}^{2} + λ {‖ G ‖}_{1, 1}}

where

{‖ \cdot ‖}_{F}

is the Frobenius norm of a matrix, and

λ > 0

is a regularization parameter.

{‖ G ‖}_{1, 1} = {‖ vec (G) ‖}_{1}

is the

l_{1, 1}

(pseudo) matrix norm [18].…”

Section: Phase Error Correction For Approximated Observation-basedmentioning

confidence: 99%

See 1 more Smart Citation

Phase Error Correction for Approximated Observation-Based Compressed Sensing Radar Imaging

Liu

Zhou

et al. 2017

Sensors

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Defocus of the reconstructed image of synthetic aperture radar (SAR) occurs in the presence of the phase error. In this work, a phase error correction method is proposed for compressed sensing (CS) radar imaging based on approximated observation. The proposed method has better image focusing ability with much less memory cost, compared to the conventional approaches, due to the inherent low memory requirement of the approximated observation operator. The one-dimensional (1D) phase error correction for approximated observation-based CS-SAR imaging is first carried out and it can be conveniently applied to the cases of random-frequency waveform and linear frequency modulated (LFM) waveform without any a priori knowledge. The approximated observation operators are obtained by calculating the inverse of Omega-K and chirp scaling algorithms for random-frequency and LFM waveforms, respectively. Furthermore, the 1D phase error model is modified by incorporating a priori knowledge and then a weighted 1D phase error model is proposed, which is capable of correcting two-dimensional (2D) phase error in some cases, where the estimation can be simplified to a 1D problem. Simulation and experimental results validate the effectiveness of the proposed method in the presence of 1D phase error or weighted 1D phase error.

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Section: Simulation and Experimental Resultsmentioning

confidence: 99%

“…Based on Equation (27), we acquire the approximated observation-based CS-SAR model (named as CS-Omega-K [16,17]) as follow:

\min_{G} {{‖ boldnormal S - normalI (G) ‖}_{F}^{2} + λ {‖ G ‖}_{1, 1}}

where

{‖ \cdot ‖}_{F}

is the Frobenius norm of a matrix, and

λ > 0

is a regularization parameter.

{‖ G ‖}_{1, 1} = {‖ vec (G) ‖}_{1}

is the

l_{1, 1}

(pseudo) matrix norm [18].…”

Section: Phase Error Correction For Approximated Observation-basedmentioning

confidence: 99%

Phase Error Correction for Approximated Observation-Based Compressed Sensing Radar Imaging

Liu

Zhou

et al. 2017

Sensors

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“…In fact, the reconstruction accuracy and sparsity of the solution is influenced by regularization coefficient λ in (13) [18]. When the optimization is solved in each iteration, the sparsity representation of the signal will be more obvious, so each iteration will be more conducive to obtaining highresolution image when regularization coefficient of the model can update adaptively.…”

Section: Optimization Imaging Model Based On Mixed Norm Sparse Constr...mentioning

confidence: 99%

An Adaptive Sparse Constraint ISAR High Resolution Imaging Algorithm Based on Mixed Norm

Song¹,

Chen²,

Li³

2022

RADIOENGINEERING

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Based on the sparsity of inverse synthetic aperture radar (ISAR) signal, in this paper, a novel high resolution imaging algorithm is proposed. In this method, an optimal ISAR signal model based on mixed norm is established by using compressed sensing theory. The highresolution ISAR image with short coherent accumulation time is realized by solving the optimization model. The main advantages of the proposed approach are: The model makes use of the l2,0 mixed norm to realize faster convergence and improve the computational speed of the model solution obviously. Moreover, according to the result sparsity of each iteration under arbitrary noise, the regularization coefficient in the model can be adjusted adaptively, which avoids the complex process of repeated attempts, otherwise, the optimal coefficient needs to be estimated and attempted by the statistical characteristics of the noise and signal. The effectiveness of the proposed method is verified by simulated and measured data.

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