Efficient Radar Emitters Scheme Recognition Based on a Novel SVM Algorithm

Abstract:

This paper proposed a efficient radar emitter signal scheme recognition method using a novel one-class SVM based Bayesian classification algorithm. First, it is proven that the solution of one-class SVM using the Gaussian kernel can be normalized as an estimate of probability density, and the probability density is used to construct the two-class and multi-class Bayesian clusters. The statistical characterization parameters of the multi radar emitter signals are extracted as the input feature vectors of the one-class SVM. Simulation result showed that the correct emitter scheme classification probability of the proposed classifier is comparable to traditional multi-class SVM classifier and better than the ANN based method. However, in the condition of large emitter mod class amount and large amount of training samples of each emitter signal class, the calculation amount of training and storage is only 0.5 percent of the traditional SVM classifier, which lead to less training time for the new classifier. In a word, the new mod scheme recognition method can be widely used in signal recognition field.

• Periodical: Advanced Engineering Forum (Volume 4)
• Pages: 232-237
• DOI: https://doi.org/10.4028/www.scientific.net/AEF.4.232
• Online since: June 2012
• Authors: Qiang Liu
• Keywords: Bayesian Classifier, One-Class SVM, Radar Emitter Mod Scheme, Recognition Probabilitiy

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