基于證據(jù)組合的通信信號(hào)調(diào)制類型的分類識(shí)別
RECOGNITION OF COMMUNICATION SIGNALS BASED ON EVIDENCE COMBINATION
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摘要: 該文應(yīng)用Dempster-Shafer理論,提出了一種有效的證據(jù)分配和組合證據(jù)最佳區(qū)間的方法,來(lái)實(shí)現(xiàn)通信信號(hào)調(diào)制類型的分類識(shí)別,在低信噪比的情況下,識(shí)別率得到較大提高,并且降低了分類器設(shè)計(jì)的復(fù)雜性。計(jì)算機(jī)模擬結(jié)果證實(shí)了此方法的有效性。Abstract: In this paper, several methods of evidence assignment and combination of optimum interval with Dempster-Shafer theory are proposed to identify communication signals. Under low SNR circumstance, the identifiability is highly promoted. Moreover, the design of classifier is simplified. The availabilities of these methods are verified by computer simulation.
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A.K. Nand, E. E. Azzouz, Algorithms for automatic modulation recognition of communicationsignals, IEEE Trans. on Comm., 1998, 46(4), 434-436.[2]E.J. Mandler, J. Schurmann, Combining the classification result of independent classifiers basedon the Dempster/Shafer theory of evidence, Pattern Recognition and Artificial Intelligence, North-Holland, Elsevier Science Publishers B. V., 1988, 381-393.[3]S.C.A. Thomopoulos, Theories in distributed decision fusion: comparison and generalization,Sensor fusion III:3-D perception and recognition, SPIE, 1383, 1990, 623-634.[4]S.C.A. Thomopoulos, Sensor integration and data fusion, Journal of Robotic Systems, 1990,7(3), 337-372.[5]S. Haykin, Neural Networks, A Comprehensive Foundation, New York, MacMillan, 1994, Ch.6. ［6］通信信號(hào)特征分析研究,研究報(bào)告,成都,電子科技大學(xué),1998. -
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