ISAR Imaging via Sparse Probing Frequencies

Wang, Hongxian; Quan, Yinghui; Xing, Mengdao; Shou-hong, Zhang

doi:10.1109/lgrs.2010.2085076

Cited by 70 publications

(53 citation statements)

References 13 publications

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“…times instead of N [6][7]. Applying a linear measurement process to s , we have  y=ΑΦs + n Ψs + n , where Α is a random measurement matrix, Ψ is defined as the dictionary of size MN  , and n is additive noise.…”

Section: High Resolution 2d Reconstruction Via Cs Consider Amentioning

confidence: 99%

High Resolution RD Reconstruction for Two-dimension Sparse HF Radar

Jin¹,

Liu²

2016

Proceedings of the 2016 International Conference on Sensor Network and Computer Engineering

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Abstract.A novel high resolution range-Doppler (RD) reconstruction method for high frequency (HF) radar via compressed sensing (CS) is proposed. In this framework, discontinuous frequency sub-bands and incomplete pulse bursts are used to reconstruct high resolution range profiles (HRRPs) and Doppler spectrum simultaneously. The method is capable of effective suppression of sidelobes introduced by bandwidth discontinuity and preservation of temporal coherence of the signal in Doppler analysis despite the time samples deficiency. Simulation and experiment results have demonstrated the effectiveness of the proposed method.

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Section: High Resolution 2d Reconstruction Via Cs Consider Amentioning

confidence: 99%

High Resolution RD Reconstruction for Two-dimension Sparse HF Radar

Jin¹,

Liu²

2016

Proceedings of the 2016 International Conference on Sensor Network and Computer Engineering

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“…Inverse synthetic aperture radar (ISAR) has been widely used for targets imaging in both military and civilian fields due to all day and all weather imaging ability [1][2][3]. High range resolution is achieved by transmitting wideband signals, while high cross-range resolution is obtained by the coherent processing interval.…”

Section: Introductionmentioning

confidence: 99%

Isar Imaging Based on Iterative Reweighted Lp Block Sparse Reconstruction Algorithm

Feng¹,

Zhang²

2016

PIER M

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Abstract-Sparse signal recovery algorithms can be used to improve radar imaging quality by using the sparse property of strong scatterers. Traditional sparse inverse synthetic aperture radar (ISAR) imaging algorithms mainly consider the recovery of sparse scatterers. However, the scatterers of an ISAR target usually exhibit block or group sparse structure. By utilizing the inherent block sparse structure of ISAR target images, an iterative reweighted l p (0 < p ≤ 1) block sparse signal recovery algorithm is proposed to enhance imaging quality in this paper. Firstly, an ISAR imaging signal model is established with the aid of sparse basis, and the imaging is mathematically converted into block reweighted cost function optimization problem. Then, an iterative algorithm is used to solve the reweighted function minimization problem. In each iteration, the weights are updated based on the closed form solution of the previous iteration. The proposed method is effective to exploit the underlying block sparse structures which does not need the prior knowledge of the number of the blocks. Real data ISAR imaging results are provided to verify that the proposed algorithm in this paper can achieve better images than the images obtained by several popular sparse signal recovery algorithms.

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“…It has been shown in [1]- [8] that high-resolution image can be achieved with a limited number of pulses by reformulating the ISAR or SAR imaging problem into an under-determined linear inverse scattering problem. Taking the uncertainties of the underdetermined inverse problem into consideration, three main strategies have been proposed to solve the problem under the sparsity constraint of the solution.…”

Section: Introductionmentioning

confidence: 99%

“…In the recent literature of ISAR imagery based on sparse representation, undersampling is performed for each range cell in the cross-range domain after the pulse compression. Instead of transmitting a wide-band signal and performing the conventional pulse compression in the range domain, a different and simplified radar system which transmits the sparse probing frequencies (SPF) is proposed to obtain a high-resolution ISAR image by using the convex optimization technique with l 1 -norm constraint [8].…”

Section: Introductionmentioning

confidence: 99%

“…More concretely, nonzero scatterers on the target region are continuously located along either the range or the cross-range domain in the target scene. Considering that radar transmits sparse probing frequencies as used in [8], we reformulate the ISAR imaging problem into the Bayesian framework to exploit the inherent continuity of the scatterers in the target scene for enhanced ISAR images. In contrast to the work reported in [4] and [6], where independent prior for each scatterer in target scene is simply used to impose the sparsity of the target image, we propose a correlated prior to enforce the continuity of the image.…”

Section: Introductionmentioning

confidence: 99%

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