High-resolution ISAR imaging of maneuvering targets based on sparse reconstruction

Sun, Chao; Wang, Baoping; Fang, Yang; Yang, Kaifang; Song, Zuxun

doi:10.1016/j.sigpro.2014.10.027

Cited by 33 publications

(14 citation statements)

References 40 publications

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“…e experiment shows the robustness and reliability of solving large-scale complex problems with the collaboration of multiple agents. e literature [16][17][18] uses the features of wavelet transform and multiresolution analysis to effectively suppress the window effect on reconstruction results. e reconstruction efficiency is improved.…”

Section: Related Workmentioning

confidence: 99%

Multiperspective Light Field Reconstruction Method via Transfer Reinforcement Learning

Cai

Luo

Zhou

et al. 2020

Computational Intelligence and Neuroscience

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Compared with traditional imaging, the light field contains more comprehensive image information and higher image quality. However, the available data for light field reconstruction are limited, and the repeated calculation of data seriously affects the accuracy and the real-time performance of multiperspective light field reconstruction. To solve the problems, this paper proposes a multiperspective light field reconstruction method based on transfer reinforcement learning. Firstly, the similarity measurement model is established. According to the similarity threshold of the source domain and the target domain, the reinforcement learning model or the feature transfer learning model is autonomously selected. Secondly, the reinforcement learning model is established. The model uses multiagent (i.e., multiperspective) Q-learning to learn the feature set that is most similar to the target domain and the source domain and feeds it back to the source domain. This model increases the capacity of the source-domain samples and improves the accuracy of light field reconstruction. Finally, the feature transfer learning model is established. The model uses PCA to obtain the maximum embedding space of source-domain and target-domain features and maps similar features to a new space for label data migration. This model solves the problems of multiperspective data redundancy and repeated calculations and improves the real-time performance of maneuvering target recognition. Extensive experiments on PASCAL VOC datasets demonstrate the effectiveness of the proposed algorithm against the existing algorithms.

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Section: Related Workmentioning

confidence: 99%

Multiperspective Light Field Reconstruction Method via Transfer Reinforcement Learning

Cai

Luo

Zhou

et al. 2020

Computational Intelligence and Neuroscience

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“…On multipling S(f, ) and (6), yields TRT. Taking TRT inverse fourier transform with variable f gives (7) Compare (7) and the compressed signal (3), the motion parameters can be estimated with low computational complexity. The TRT Transformed signal is applied to SGRFT function and simplification yields (8) SGRFT( exp .…”

Section: Parameters Estimation Via Trt-sgrftmentioning

confidence: 99%

Statistical Analysis of Target Detection using TRT-SGRFT

Jagadesh*¹,

Rani²

2019

IJRTE

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This paper proposes novel methodology for target detection and motion parameter estimation for a maneuvering target. Noise like interference may inculcate few difficulties such as Range Migration (RM) and Doppler Frequency Migration (DFM). This paper proposes a novel method to overcome the issue. The method is to use Time Reversing Transform (TRT) and special generalized Radon Fourier transform (SGRFT), i.e., TRT-SGRFT. TRT separates the motion parameters and reduces the clutter. Then the SGRFT operation is employed to estimate part of the motion parameters. This method ensures a good tradeoff between the computational cost and estimation performance. Finally, simulations results are discussed to demonstrate the numerical results.

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“…According to CS,

g

can be recovered by solving

L

optimization problems corresponding to each channel or sub-aperture:

\min {‖ g_{l} ‖}_{0} suject to {‖ s_{l}^{C S} bold- A_{l}^{C S} g_{l} ‖}_{2}^{2} \leq ξ_{l} (l = 1, 2, \dots L)

where

{‖ \cdot ‖}_{0}

is defined as the number of the non-zero elements (also called sparsity or

l_{0}

norm),

\min false(bold\cdot false)

denotes the minimization,

g_{l}

is the backward scattering estimator of the l -th channel or sub-aperture with respect to

A_{l}^{C S}

and

ξ_{l}

is the noise level of the l -th channel or sub-aperture, which can be estimated by the range bins containing only noise [9]. When

L

optimization problems are successively solved, SAR images of all channels or sub-apertures are obtained accordingly.…”

Section: Multichannel and Wide-angle Sar Imaging Based On Csmentioning

confidence: 99%

Multichannel and Wide-Angle SAR Imaging Based on Compressed Sensing

Sun

Wang

Fang

et al. 2017

Sensors

Self Cite

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The multichannel or wide-angle imaging performance of synthetic aperture radar (SAR) can be improved by applying the compressed sensing (CS) theory to each channel or sub-aperture image formation independently. However, this not only neglects the complementary information between signals of each channel or sub-aperture, but also may lead to failure in guaranteeing the consistency of the position of a scatterer in different channel or sub-aperture images which will make the extraction of some scattering information become difficult. By exploiting the joint sparsity of the signal ensemble, this paper proposes a novel CS-based method for joint sparse recovery of all channel or sub-aperture images. Solving the joint sparse recovery problem with a modified orthogonal matching pursuit algorithm, the recovery precision of scatterers is effectively improved and the scattering information is also preserved during the image formation process. Finally, the simulation and real data is used for verifying the effectiveness of the proposed method. Compared with single channel or sub-aperture independent CS processing, the proposed method can not only obtain better imaging performance with fewer measurements, but also preserve more valuable scattering information for target recognition.

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High-resolution ISAR imaging of maneuvering targets based on sparse reconstruction

Cited by 33 publications

References 40 publications

Multiperspective Light Field Reconstruction Method via Transfer Reinforcement Learning

Multiperspective Light Field Reconstruction Method via Transfer Reinforcement Learning

Statistical Analysis of Target Detection using TRT-SGRFT

Multichannel and Wide-Angle SAR Imaging Based on Compressed Sensing

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