Radar Emitter Signal Identification Based on SLIDE+SVM

Authors

Yingkun Huang, College of Electrical Engineering Southwest Jiaotong University, Chengdu 610031, China;
Weidong Jin, College of Electrical Engineering Southwest Jiaotong University, Chengdu 610031, China;

Abstract

Abstract: For the deficiency of traditional techniques of emitter signal feature extraction which heavily rely on experience, a model of radar emitting signal identification based on feature self-learning was proposed. This model consists of following 2 parts. Firstly, transform radar signal into frequency domain, then reduce signal dimension by using improved Piecewise Aggregate Approximation (PAA) method. Secondly, create the model of multi-layer Liner Denoiser (LIDE) to feature learning by using unsupervised training method. The validity of model was verified by simulating 5 different kinds of emitting signal with the outcome that excellent identification accuracy could be achieved at low SNR levels.

Recommended Citation

Huang, Yingkun and Jin, Weidong (2020) "Radar Emitter Signal Identification Based on SLIDE+SVM," Journal of System Simulation: Vol. 29: Iss. 9, Article 10.
DOI: 10.16182/j.issn1004731x.joss.201709010
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss9/10

First Page

1944

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201709010

Last Page

1949

CLC

TN973

Recommended Citation

Huang Yingkun, Jin Weidong. Radar Emitter Signal Identification Based on SLIDE+SVM[J]. Journal of System Simulation, 2017, 29(9): 1944-1949.

DOI

10.16182/j.issn1004731x.joss.201709010