scholarly journals High-resolution Distributed ISAR Imaging based on Sparse Representation

2018 ◽  
Vol 214 ◽  
pp. 02004
Author(s):  
Yuanyuan Li ◽  
Yaowen Fu ◽  
Wenpeng Zhang
Keyword(s):  
Sparse Representation ◽  
Single Channel ◽  
Interpolation Method ◽  
Matching Pursuit ◽  
Range Resolution ◽  
Scattering Field ◽  
Isar Imaging ◽  
Matching Pursuit Algorithm ◽  
Strong Scattering ◽  
Spatial Sparsity

Distributed ISAR technique has the potential to increase the cross-range resolution by exploiting multi-channel echoes from distributed virtual equivalent sensors. In the existing imaging approaches, the echoes acquired from different sensors are rearranged into an equivalent single-channel ISAR signal. Then, the missing data between the observation angles of any two adjacent sensors is restored by interpolation. However, the interpolation method can be very inaccurate when the gap is large or the signal-to-noise (SNR) of echoes is low. In this paper, we discuss sparse representation of distributed ISAR echoes since the scattering field of the target is usually composed of only a limited number of strong scattering centres, representing strong spatial sparsity. Then, by using sparse algorithm (Orthogonal Matching Pursuit algorithm, OMP), the positions and amplitudes of the scattering points in every range bin can be recovered and the final ISAR image with high cross-range resolution can be obtained. Results show the effectiveness of the proposed method.

scholarly journals Joint Sparsity Constraint Interferometric ISAR Imaging for 3-D Geometry of Near-Field Targets with Sub-Apertures

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3750 ◽  
Author(s):  
Yang Fang ◽  
Baoping Wang ◽  
Chao Sun ◽  
Shuzhen Wang ◽  
Jiansheng Hu ◽  
...  
Keyword(s):  
Relative Phase ◽  
Single Channel ◽  
Matching Pursuit ◽  
Near Field ◽  
Joint Sparsity ◽  
Electromagnetic Data ◽  
Scattering Center ◽  
Isar Imaging ◽  
Imaging Approach ◽  
Metric Function

This paper proposes a new interferometric near-field 3-D imaging approach based on multi-channel joint sparse reconstruction to solve the problems of conventional methods, i.e., the irrespective correlation of different channels in single-channel independent imaging which may lead to deviated positions of scattering points, and the low accuracy of imaging azimuth angle for real anisotropic targets. Firstly, two full-apertures are divided into several sub-apertures by the same standard; secondly, the joint sparse metric function is constructed based on scattering characteristics of the target in multi-channel status, and the improved Orthogonal Matching Pursuit (OMP) method is used for imaging solving, so as to obtain high-precision 3-D image of each sub-aperture; thirdly, comprehensive sub-aperture processing is performed using all sub-aperture 3-D images to obtain the final 3-D images; finally, validity of the proposed approach is verified by using simulation electromagnetic data and data measured in the anechoic chamber. Experimental results show that, compared with traditional interferometric ISAR imaging approaches, the algorithm proposed in this paper is able to provide a higher accuracy in scattering center reconstruction, and can effectively maintain relative phase information of channels.

scholarly journals A Broadband Spectrum Sensing Algorithm in TDCS Based on ICoSaMP Reconstruction

2018 ◽  
Vol 173 ◽  
pp. 03073
Author(s):  
Liu Yang ◽  
Ren Qinghua ◽  
Xu Bingzheng ◽  
Li Xiazhao
Keyword(s):  
Time Use ◽  
Matching Pursuit ◽  
Reconstruction Algorithm ◽  
Transform Domain ◽  
Low Snr ◽  
System Noise ◽  
Broadband Spectrum ◽  
Matching Pursuit Algorithm ◽  
The Mean ◽  
Occupancy Rates

In order to solve the problem that the wideband compressive sensing reconstruction algorithm cannot accurately recover the signal under the condition of blind sparsity in the low SNR environment of the transform domain communication system. This paper use band occupancy rates to estimate sparseness roughly, at the same time, use the residual ratio threshold as iteration termination condition to reduce the influence of the system noise. Therefore, an ICoSaMP(Improved Compressive Sampling Matching Pursuit) algorithm is proposed. The simulation results show that compared with CoSaMP algorithm, the ICoSaMP algorithm increases the probability of reconstruction under the same SNR environment and the same sparse degree. The mean square error under the blind sparsity is reduced.

Incipient Broken Rotor Bar Detection in Induction Motors Using Vibration Signals and the Orthogonal Matching Pursuit Algorithm

2018 ◽  
Vol 67 (9) ◽  
pp. 2058-2068 ◽  
Author(s):  
Carlos Morales-Perez ◽  
Jose Rangel-Magdaleno ◽  
Hayde Peregrina-Barreto ◽  
Juan Pablo Amezquita-Sanchez ◽  
Martin Valtierra-Rodriguez
Keyword(s):  
Induction Motors ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Vibration Signals ◽  
Broken Rotor Bar ◽  
Matching Pursuit Algorithm

Image Denoising Using Sparse Representation and Principal Component Analysis

2021 ◽  
pp. 2250033
Author(s):  
Maryam Abedini ◽  
Horriyeh Haddad ◽  
Marzieh Faridi Masouleh ◽  
Asadollah Shahbahrami
Keyword(s):  
Principal Component Analysis ◽  
Sparse Representation ◽  
Image Denoising ◽  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Structural Similarity ◽  
Component Analysis ◽  
Block Matching ◽  
Discrete Wavelet

This study proposes an image denoising algorithm based on sparse representation and Principal Component Analysis (PCA). The proposed algorithm includes the following steps. First, the noisy image is divided into overlapped [Formula: see text] blocks. Second, the discrete cosine transform is applied as a dictionary for the sparse representation of the vectors created by the overlapped blocks. To calculate the sparse vector, the orthogonal matching pursuit algorithm is used. Then, the dictionary is updated by means of the PCA algorithm to achieve the sparsest representation of vectors. Since the signal energy, unlike the noise energy, is concentrated on a small dataset by transforming into the PCA domain, the signal and noise can be well distinguished. The proposed algorithm was implemented in a MATLAB environment and its performance was evaluated on some standard grayscale images under different levels of standard deviations of white Gaussian noise by means of peak signal-to-noise ratio, structural similarity indexes, and visual effects. The experimental results demonstrate that the proposed denoising algorithm achieves significant improvement compared to dual-tree complex discrete wavelet transform and K-singular value decomposition image denoising methods. It also obtains competitive results with the block-matching and 3D filtering method, which is the current state-of-the-art for image denoising.

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