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2019-09-20
Backward Cloud Model Based Feature Extraction of Aircraft Echoes and Target Classification
By
Qiusheng Li,

    Professor Qiusheng Li

    School of Physics and Electronic Information

    Gannan Normal University

    China

    Homepage

Li Wang

    Dr. Li Wang

    School of Physical Education

    Gannan Normal University

    China

    Homepage

Progress In Electromagnetics Research M, Vol. 85, 115-123, 2019
doi:10.2528/PIERM19072301 | Google Scholar

Abstract
As a kind of complicated targets, the nonrigid vibration of aircraft, their attitude change, and the rotation of their ro-tating parts will induce complicated nonlinear modulation on their echoes from low-resolution radars. These kinds of modulation play an important role in target classification. However, due to the influence of clutter and noise, these kinds of modulation have the characteristics of fuzziness and randomness. As a quantitative to qualitative conversion model based on traditional probability statis-tics theory and fuzzy theory, backward cloud model can be used to model and analyze the modulation characteristics of the conven-tional low-resolution radar echoes from aircraft targets. By considering the sample values of the echo data as individual cloud drop-lets, the paper extracts the cloud digital features such as the expectation, entropy and hyper-entropy of each group of echo data, and investigates the application of these features in aircraft target classification based on support vector machine. The research results show that the backward cloud model can describe the aircraft echoes well, and the echo cloud digital features can be effectively used for the classification and identification of aircraft targets.
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Citation
Qiusheng Li, and Li Wang, "Backward Cloud Model Based Feature Extraction of Aircraft Echoes and Target Classification," Progress In Electromagnetics Research M, Vol. 85, 115-123, 2019.
doi:10.2528/PIERM19072301
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