Compressive Sensing and Reconstruction’s Algorithm on Radar Mimo

Randrianandrasana Marie Emile; Randriamitantsoa Paul Auguste

doi:doi:10.11648/j.ajece.20220602.13

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Compressive Sensing and Reconstruction’s Algorithm on Radar Mimo

Randrianandrasana Marie Emile, Randriamitantsoa Paul Auguste

Published in American Journal of Electrical and Computer Engineering (Volume 6, Issue 2)

Received: 5 July 2022 Accepted: 10 August 2022 Published: 31 August 2022

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Abstract

Two advanced technic appears concerning the digital processing: the detection system RADAR and a compression technic named the Compressive Sensing (CS). This modern acquisition technic combined with reconstruction, offers multiple advantages. This research explains a new technic of acquisition with compression: the Analog to Information Converter (AIC). The standard method uses Analog to Digital converters (ADC). This method named AIC can defeat even the Nyquist Shannon criteria, by using advanced transformation. This article shows the application of compressed sensing MIMO RADAR. Based on the propriety of the signal, we study criteria of mathematics’ compressibility, to the choice of the methods, the two algorithm of reconstruction that we use named Matching Pursuit (MP) and Orthogonal Matching Pursuit (OMP). So, we could have compressive sensing with Non-Uniform Sampling that we named CS-NUS on this article. Our contribution consists of using detection of the multiple targets combined with the CS. For multiple targets, we use the Principal Component Analysis (PCA) to send the signal and recover it. The Signal to Noise Ratio (SNR) and Compressive Ratio (CR) permit to conclude that Orthogonal Matching Pursuit offers a best performance than Matching Pursuit. The Matching Pursuit algorithm cited previously gives a good time reconstruction processing but not offers a good quality of reconstruction.

Published in	American Journal of Electrical and Computer Engineering (Volume 6, Issue 2)
DOI	10.11648/j.ajece.20220602.13
Page(s)	68-80
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

CS, PCA, Radar MIMO, MP, OMP

References

[1]	N. Levanon, E. Mozen, RADAR signal, John Wiley and Sons Inc, 2004.
[2]	N. Pandey, Beamforming in MIMO Radar, Department of Electronics and Communication Engineering National Institute of Technology Rourkela 2014.
[3]	Curry, G. R., Radar System Performance Modeling, Artech House, Norwood, 2001.
[4]	M. Lustig, David L Donoho, J. M Santos, John M Pauly, Compressed sensing mri, IEEE Signal Processing Magazine, 2008.
[5]	M. A Davenport, Marco F Duarte, Yonina C Eldar, Gitta Kutyniok, Introduction to compressed sensing, Preprint, 2011.
[6]	E. Candes, J. Romberg, Sparsity and incoherence in compressive sampling, Inverse problems, 2007.
[7]	S. Qaisar, R. Muhammad Bilal, Compressive sensing: From theory to applications, a survey. Journal of Communications and networks, 2013.
[8]	S. G Mallat, Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on signal processing, 1993.
[9]	Y. Chandra Pati, R. Rezaiifar, Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. In Signals, Systems and Computers, IEEE Transactions on signal processing, 1993.
[10]	M. A Lexa, M. E Davies, J. S Thompson, Reconciling compressive sampling systems for spectrally sparse continuous-time signals, IEEE Transactions on Signal Processing, 2012.
[11]	M. Mishali, Yonina C Eldar, From theory to practice: Sub-nyquist sampling of sparse wideband analog signals; Selected Topics in Signal Processing, IEEE Journal of, 2010.
[12]	E. Candes, Sparse Representations for Radar, URL: http://www.raeng.org.uk/publications/other/.
[13]	J. Wang, S. Kwon, P. Li, B. Shim, Recovery of sparse signals via generalized orthogonal matching pursui, A new analysis. IEEE Transactions on Signal Processing, 2016.
[14]	S. Rapuano, Analog-to-information converters research trends and open problems, In 2016 26th International Conference Radioelektronika, 2016.
[15]	Ankit Kundu, Pradosh K Roy «Sparse signal recovery from nonadaptive linear measurements», 2013.
[16]	SparseLab, URL: https://sparselab.stanford.edu/.

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APA Style

Randrianandrasana Marie Emile, Randriamitantsoa Paul Auguste. (2022). Compressive Sensing and Reconstruction’s Algorithm on Radar Mimo. American Journal of Electrical and Computer Engineering, 6(2), 68-80. https://doi.org/10.11648/j.ajece.20220602.13

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ACS Style

Randrianandrasana Marie Emile; Randriamitantsoa Paul Auguste. Compressive Sensing and Reconstruction’s Algorithm on Radar Mimo. Am. J. Electr. Comput. Eng. 2022, 6(2), 68-80. doi: 10.11648/j.ajece.20220602.13

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AMA Style

Randrianandrasana Marie Emile, Randriamitantsoa Paul Auguste. Compressive Sensing and Reconstruction’s Algorithm on Radar Mimo. Am J Electr Comput Eng. 2022;6(2):68-80. doi: 10.11648/j.ajece.20220602.13

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@article{10.11648/j.ajece.20220602.13,
  author = {Randrianandrasana Marie Emile and Randriamitantsoa Paul Auguste},
  title = {Compressive Sensing and Reconstruction’s Algorithm on Radar Mimo},
  journal = {American Journal of Electrical and Computer Engineering},
  volume = {6},
  number = {2},
  pages = {68-80},
  doi = {10.11648/j.ajece.20220602.13},
  url = {https://doi.org/10.11648/j.ajece.20220602.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajece.20220602.13},
  abstract = {Two advanced technic appears concerning the digital processing: the detection system RADAR and a compression technic named the Compressive Sensing (CS). This modern acquisition technic combined with reconstruction, offers multiple advantages. This research explains a new technic of acquisition with compression: the Analog to Information Converter (AIC). The standard method uses Analog to Digital converters (ADC). This method named AIC can defeat even the Nyquist Shannon criteria, by using advanced transformation. This article shows the application of compressed sensing MIMO RADAR. Based on the propriety of the signal, we study criteria of mathematics’ compressibility, to the choice of the methods, the two algorithm of reconstruction that we use named Matching Pursuit (MP) and Orthogonal Matching Pursuit (OMP). So, we could have compressive sensing with Non-Uniform Sampling that we named CS-NUS on this article. Our contribution consists of using detection of the multiple targets combined with the CS. For multiple targets, we use the Principal Component Analysis (PCA) to send the signal and recover it. The Signal to Noise Ratio (SNR) and Compressive Ratio (CR) permit to conclude that Orthogonal Matching Pursuit offers a best performance than Matching Pursuit. The Matching Pursuit algorithm cited previously gives a good time reconstruction processing but not offers a good quality of reconstruction.},
 year = {2022}
}

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TY - JOUR
T1 - Compressive Sensing and Reconstruction’s Algorithm on Radar Mimo
AU - Randrianandrasana Marie Emile
AU - Randriamitantsoa Paul Auguste
Y1 - 2022/08/31
PY - 2022
N1 - https://doi.org/10.11648/j.ajece.20220602.13
DO - 10.11648/j.ajece.20220602.13
T2 - American Journal of Electrical and Computer Engineering
JF - American Journal of Electrical and Computer Engineering
JO - American Journal of Electrical and Computer Engineering
SP - 68
EP - 80
PB - Science Publishing Group
SN - 2640-0502
UR - https://doi.org/10.11648/j.ajece.20220602.13
AB - Two advanced technic appears concerning the digital processing: the detection system RADAR and a compression technic named the Compressive Sensing (CS). This modern acquisition technic combined with reconstruction, offers multiple advantages. This research explains a new technic of acquisition with compression: the Analog to Information Converter (AIC). The standard method uses Analog to Digital converters (ADC). This method named AIC can defeat even the Nyquist Shannon criteria, by using advanced transformation. This article shows the application of compressed sensing MIMO RADAR. Based on the propriety of the signal, we study criteria of mathematics’ compressibility, to the choice of the methods, the two algorithm of reconstruction that we use named Matching Pursuit (MP) and Orthogonal Matching Pursuit (OMP). So, we could have compressive sensing with Non-Uniform Sampling that we named CS-NUS on this article. Our contribution consists of using detection of the multiple targets combined with the CS. For multiple targets, we use the Principal Component Analysis (PCA) to send the signal and recover it. The Signal to Noise Ratio (SNR) and Compressive Ratio (CR) permit to conclude that Orthogonal Matching Pursuit offers a best performance than Matching Pursuit. The Matching Pursuit algorithm cited previously gives a good time reconstruction processing but not offers a good quality of reconstruction.
VL - 6
IS - 2
ER -

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Author Information

Randrianandrasana Marie Emile

Department of Telecommunication, Antsirabe Vankinankaratra High Education Institute, University of Antananarivo, Antananarivo, Madagascar
Randriamitantsoa Paul Auguste

Department of Telecommunication, High School Polytechnic of Antananarivo, University of Antananarivo, Antananarivo, Madagascar

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APA Style

Randrianandrasana Marie Emile, Randriamitantsoa Paul Auguste. (2022). Compressive Sensing and Reconstruction’s Algorithm on Radar Mimo. American Journal of Electrical and Computer Engineering, 6(2), 68-80. https://doi.org/10.11648/j.ajece.20220602.13

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ACS Style

Randrianandrasana Marie Emile; Randriamitantsoa Paul Auguste. Compressive Sensing and Reconstruction’s Algorithm on Radar Mimo. Am. J. Electr. Comput. Eng. 2022, 6(2), 68-80. doi: 10.11648/j.ajece.20220602.13

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AMA Style

Randrianandrasana Marie Emile, Randriamitantsoa Paul Auguste. Compressive Sensing and Reconstruction’s Algorithm on Radar Mimo. Am J Electr Comput Eng. 2022;6(2):68-80. doi: 10.11648/j.ajece.20220602.13

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@article{10.11648/j.ajece.20220602.13,
  author = {Randrianandrasana Marie Emile and Randriamitantsoa Paul Auguste},
  title = {Compressive Sensing and Reconstruction’s Algorithm on Radar Mimo},
  journal = {American Journal of Electrical and Computer Engineering},
  volume = {6},
  number = {2},
  pages = {68-80},
  doi = {10.11648/j.ajece.20220602.13},
  url = {https://doi.org/10.11648/j.ajece.20220602.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajece.20220602.13},
  abstract = {Two advanced technic appears concerning the digital processing: the detection system RADAR and a compression technic named the Compressive Sensing (CS). This modern acquisition technic combined with reconstruction, offers multiple advantages. This research explains a new technic of acquisition with compression: the Analog to Information Converter (AIC). The standard method uses Analog to Digital converters (ADC). This method named AIC can defeat even the Nyquist Shannon criteria, by using advanced transformation. This article shows the application of compressed sensing MIMO RADAR. Based on the propriety of the signal, we study criteria of mathematics’ compressibility, to the choice of the methods, the two algorithm of reconstruction that we use named Matching Pursuit (MP) and Orthogonal Matching Pursuit (OMP). So, we could have compressive sensing with Non-Uniform Sampling that we named CS-NUS on this article. Our contribution consists of using detection of the multiple targets combined with the CS. For multiple targets, we use the Principal Component Analysis (PCA) to send the signal and recover it. The Signal to Noise Ratio (SNR) and Compressive Ratio (CR) permit to conclude that Orthogonal Matching Pursuit offers a best performance than Matching Pursuit. The Matching Pursuit algorithm cited previously gives a good time reconstruction processing but not offers a good quality of reconstruction.},
 year = {2022}
}

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