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Electronics
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Compressed Sensing in Signal Processing and Imaging
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Compressed Sensing in Signal Processing and Imaging

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 3788

Special Issue Editors

Prof. Dr. Andon Lazarov

E-Mail Website1 Website2
Guest Editor
Information Department, Nikola Vaptsarov Naval Academy, 9002 Varna, Bulgaria
Interests: SAR; ISAR systems; models; imaging algorithms
Special Issues, Collections and Topics in MDPI journals
Dr. Jacek Misiurewicz

E-Mail Website1 Website2
Guest Editor
Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland
Interests: signal processing; non-uniform sampling in radar; compressed sensing; noise and passive radar technology;radar resource management; track before detect techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advancement of compressive sensing and sparse signal processing has received a lot of attention in academia and industry over the last two decades. The objective of this Special Issue is to present studies in the field of compressed sensing in signal and image processing with emphasis on CS theory, methods, and algorithms applicable. Therefore, academics, students, and researchers are invited to submit their manuscripts to this Special Issue and contribute details of their models, proposals, reviews, and studies.

Potential topics include but are not limited to the following：

  • Target detection and tracking based on compressed sensing
  • Super-resolution for unmanned aerial vehicle (UAV) swarms based on compressed sensing and tensors
  • Compressed sensing and tensor for 6G communication
  • Compressed sensing and tensors for communication coexistence
  • Detection and estimation of signals using compressive sensing
  • Recent applications of compressive sensing
  • Hardware implementation of compressive sensing
  • Deep compressive sensing
Prof. Dr. Andon Lazarov

Dr. Jacek Misiurewicz
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

12 pages, 5738 KiB  
Article
Group Class Residual ℓ1-Minimization on Random Projection Sparse Representation Classifier for Face Recognition
by Susmini Indriani Lestariningati, Andriyan Bayu Suksmono, Ian Joseph Matheus Edward and Koredianto Usman
Electronics 2022, 11(17), 2723; https://doi.org/10.3390/electronics11172723 - 30 Aug 2022
Cited by 2 | Viewed by 1231
Abstract
Sparse Representation-based Classification (SRC) has been seen to be a reliable Face Recognition technique. The ℓ1 Bayesian based on the Lasso algorithm has proven to be most effective in class identification and computation complexity. In this paper, we revisit classification algorithm and [...] Read more.
Sparse Representation-based Classification (SRC) has been seen to be a reliable Face Recognition technique. The ℓ1 Bayesian based on the Lasso algorithm has proven to be most effective in class identification and computation complexity. In this paper, we revisit classification algorithm and then recommend the group-based classification. The proposed modified algorithm, which is called as Group Class Residual Sparse Representation-based Classification (GCR-SRC), extends the coherency of the test sample to the whole training samples of the identified class rather than only to the nearest one of the training samples. Our method is based on the nearest coherency between a test sample and the identified training samples. To reduce the dimension of the training samples, we choose random projection for feature extraction. This method is selected to reduce the computational cost without increasing the algorithm’s complexity. From the simulation result, the reduction factor (ρ) 64 can achieve a maximum recognition rate about 10% higher than the SRC original using the downscaling method. Our proposed method’s feasibility and effectiveness are tested on four popular face databases, namely AT&T, Yale B, Georgia Tech, and AR Dataset. GCR-SRC and GCR-RP-SRC achieved up to 4% more accurate than SRC random projection with class-specific residuals. The experiment results show that the face recognition technology based on random projection and group-class-based not only reduces the dimension of the face data but also increases the recognition accuracy, indicating that it is a feasible method for face recognition. Full article
(This article belongs to the Special Issue Compressed Sensing in Signal Processing and Imaging)
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12 pages, 1213 KiB  
Article
An Iterative Phase Autofocus Approach for ISAR Imaging of Maneuvering Targets
by Binbin Wang, Hao Cha, Zibo Zhou, Huatao Tang, Lidong Sun, Baozhou Du and Lei Zuo
Electronics 2021, 10(17), 2100; https://doi.org/10.3390/electronics10172100 - 30 Aug 2021
Cited by 6 | Viewed by 1456
Abstract
Translational motion compensation and azimuth compression are two essential processes in inverse synthetic aperture radar (ISAR) imaging. The anterior process recovers coherence between pulses, during which the phase autofocus algorithm is usually used. For ISAR imaging of maneuvering targets, conventional phase autofocus methods [...] Read more.
Translational motion compensation and azimuth compression are two essential processes in inverse synthetic aperture radar (ISAR) imaging. The anterior process recovers coherence between pulses, during which the phase autofocus algorithm is usually used. For ISAR imaging of maneuvering targets, conventional phase autofocus methods cannot effectively eliminate the phase error due to the adverse influence of the quadratic phase terms caused by the target’s maneuvering motion, which leads to the blurring of ISAR images. To address this problem, an iterative phase autofocus approach for ISAR imaging of maneuvering targets is proposed in this paper. Considering the coupling between translational phase errors and quadratic phase terms, minimum entropy-based autofocus (MEA) method and adaptive modified Fourier transform (MFT) are performed iteratively to realize better imaging results. In this way, both the translational phase error and quadratic phase terms induced by target’s maneuvering motion can be compensated effectively, and the globally optimal ISAR image is obtained. Comparison ISAR imaging results indicates that the new approach achieves stable and better ISAR image under a simple procedure. Experimental results show that the image entropy of the proposed approach is 0.2 smaller than the MEA method, which validates the effectiveness of the new approach. Full article
(This article belongs to the Special Issue Compressed Sensing in Signal Processing and Imaging)
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