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      Low-resolution Airborne Radar Aircraft Target Classification

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          Abstract

          Target classification is particularly important in modern and future airborne radar. Nowadays, most investigations of radar target classification are based on wideband radar signals, which have higher requirements for SNR and radar systems, and are sensitive to the angles. Modern airborne radars require narrowband tracking and target classification; hence, an algorithm based on the narrowband fractal features and the amplitude modulation of a two-dimensional distribution is presented. Experimental data and Support Vector Machine (SVM) are used to verify the algorithm, and the classification results validate the proposed method, which show that jet aircrafts, propeller aircrafts, and helicopters can be classified with an average discrimination rate greater than 92%.

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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 August 2014
          : 3
          : 4
          : 444-449
          Affiliations
          [1 ] AVIC LEIHUA Electronic Technology Research Institute
          [2 ] National Laboratory of Radar Signal Processing, Xidian University
          Article
          b0c8105213bd456783023b69a7897221
          10.3724/SP.J.1300.2014.14075
          b1670b2a-ff97-41cd-8400-03be50a9d796

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Categories
          Technology (General)
          T1-995

          Remote sensing,Electrical engineering
          Support Vector Machine (SVM),Amplitude modulation feature,Fractal feature,Aircraft target classification,Low-resolution,Airborne radar

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