Radio ground-to-air interference signals recognition based on support vector machine

In this paper, we use support vector machine (SVM) to recognize the acoustic frequency of radio signals, in which, the SVM with the polynomial kernel function is optimized by gravitational search algorithm and selected as a classifier to recognize the acoustic frequency of radio signals, radio signals are the civil aviation radio ground-to-air interference signals, its the acoustic frequency are recognized by the optimized SVM classifier. Experiments show that our method is more accuracy and robustness than genetic algorithm to recognize radio ground-to-air interference signals.

[1]  Bin Ma,et al.  Discriminative feature extraction for speech recognition using continuous output codes , 2012, Pattern Recognit. Lett..

[2]  Hossein Nezamabadi-pour,et al.  Facing the classification of binary problems with a GSA-SVM hybrid system , 2013, Math. Comput. Model..

[3]  Nan Xu,et al.  Modulation recognition of communication signals based on high order cumulants and support vector machine , 2012 .

[4]  Abdulkadir Sengur.  Multiclass least-squares support vector machines for analog modulation classification , 2009 .

[5]  Ludovic Duponchel,et al.  Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils. , 2014, Food chemistry.

[6]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[7]  Engin Avci,et al.  A novel approach for digital radio signal classification: Wavelet packet energy-multiclass support vector machine (WPE-MSVM) , 2008, Expert Syst. Appl..

[8]  Zong Dai,et al.  Identification of protein methylation sites by coupling improved ant colony optimization algorithm and support vector machine. , 2011, Analytica chimica acta.

[9]  Ruxu Du,et al.  Fault diagnosis using support vector machine with an application in sheet metal stamping operations , 2004 .

[10]  Zhenyuan Jia,et al.  Hybrid of simulated annealing and SVM for hydraulic valve characteristics prediction , 2011, Expert Syst. Appl..

[11]  Wentao Mao,et al.  An adaptive support vector regression based on a new sequence of unified orthogonal polynomials , 2013, Pattern Recognit..

[12]  Congli Mei,et al.  Pattern Recognition of Eight Hand Motions Using Feature Extraction of Forearm EMG Signal , 2014 .

[13]  Ataollah Ebrahimzadeh,et al.  Recognition of communication signal types using genetic algorithm and support vector machines based on the higher order statistics , 2010, Digit. Signal Process..

[14]  Tai-Chiu Hsung,et al.  Wavelet based speech presence probability estimator for speech enhancement , 2012, Digit. Signal Process..

[15]  Qi Zhu,et al.  Cooperative automatic modulation recognition in cognitive radio , 2010 .

[16]  Man-Wai Mak,et al.  A study of voice activity detection techniques for NIST speaker recognition evaluations , 2014, Comput. Speech Lang..

[17]  Wei-Chiang Hong,et al.  Application of chaotic ant swarm optimization in electric load forecasting , 2010 .

[18]  Hossein Nezamabadi-pour,et al.  A simultaneous feature adaptation and feature selection method for content-based image retrieval systems , 2013, Knowl. Based Syst..

[19]  Zheng Pei,et al.  AM-FM Signal Modulation Recognition Based on the Power Spectrum , 2014 .

[20]  Sakti Prasad Ghoshal,et al.  A novel opposition-based gravitational search algorithm for combined economic and emission dispatch problems of power systems , 2012 .

[21]  Jiang Hong-mei.  Speech/non-speech detection based on the fourth order statistics , 2008 .

[22]  V. Sugumaran,et al.  Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool , 2011, Expert Syst. Appl..

[23]  M. Dougherty,et al.  Classification of speech intelligibility in Parkinson's disease , 2014 .

[24]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[25]  Sungwan Bang,et al.  Weighted Support Vector Machine Using k-Means Clustering , 2014, Commun. Stat. Simul. Comput..

[26]  Jun Huang,et al.  An energy-constrained signal subspace method for speech enhancement and recognition in white and colored noises , 1998, Speech Commun..

[27]  Zheng Pei,et al.  Support Vector Machine (SVM) Based on Membrane Computing Optimization and the Application for C-band Radio Abnormal Signal Identication , 2014 .