Radar Emitter Signal Identification Based on SLIDE+SVM

doi:10.16182/j.issn1004731x.joss.201709010

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.

Key words: radar emitter signal identification, piecewise aggregate approximation, liner denoiser, support vector machine

CLC Number:

TN973

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

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