Analiza algorytmu automatycznego rozpoznawania modulacji na potrzeby radia kognitywnego oraz systemów rozpoznania radioelektronicznego

Walenczykowska, Marta

doi:10.15199/13.2015.4.7

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2016

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“…As a classifier there is usually an artificial neural network considered [8] or decision trees. There are also a couple of articles concerning modulation recognition algorithms using the Wavelet Transform as a method of feature extraction [9][10][11][12][13][14][15][16][17]. We can divide them into categories e.g.…”

Section: Introductionmentioning

confidence: 99%

Type of modulation identification using Wavelet Transform and Neural Network

Walenczykowska¹,

Kawalec²

2016

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. Automatic recognition of the signal modulation type turned out to be useful in many areas, including electronic warfare or surveillance. The wavelet transform is an effective way to extract signal features for identification purposes. In this paper there are M-ary ASK, M-ary PSK, M-ary FSK, M-ary QAM, OOK and MSK signals analysed. The mean value, variance and central moments up to five of continuous wavelet transform (CWT) are used as signal features. The principal component analysis (PCA) is applied to reduce a number of features. A multi-layer neural network trained with backpropagation learning algorithm is considered as a classifier. There are two research variants: interclass and intraclass recognition with a wide range of signal-to-noise ratio (SNR).

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