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1Department of Telecommunication, Antsirabe Vankinankaratra High Education Institute, University of Antananarivo, Antananarivo, Madagascar
2Department of Telecommunication, High School Polytechnic of Antananarivo, University of Antananarivo, Antananarivo, Madagascar
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.
CS, PCA, Radar MIMO, MP, OMP
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
Copyright © 2022 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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