Radar Target Recognition Based on Stacked Denoising Sparse Autoencoder

Feature extraction is a key step in radar target recognition. The quality of the extracted features determines the performance of target recognition. However, obtaining the deep nature of the data is difficult using the traditional method. The autoencoder can learn features by making use of data and...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of radars = Lei da xue bao Jg. 6; H. 2; S. 149 - 156
Hauptverfasser: Zhao Feixiang, Liu Yongxiang, Huo Kai
Format: Journal Article
Sprache:Chinesisch
Englisch
Veröffentlicht: China Science Publishing & Media Ltd. (CSPM) 28.04.2017
Schlagworte:
Deep learning
Stacked denoising sparse autoencoder
Target recognition
ISSN:2095-283X, 2095-283X
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Feature extraction is a key step in radar target recognition. The quality of the extracted features determines the performance of target recognition. However, obtaining the deep nature of the data is difficult using the traditional method. The autoencoder can learn features by making use of data and can obtain feature expressions at different levels of data. To eliminate the influence of noise, the method of radar target recognition based on stacked denoising sparse autoencoder is proposed in this paper. This method can extract features directly and efficiently by setting different hidden layers and numbers of iterations. Experimental results show that the proposed method is superior to the K-nearest neighbor method and the traditional stacked autoencoder.
ISSN:2095-283X
2095-283X
DOI:10.12000/JR16151