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Recognition Of The Communication Signal Modulation Patterns Based On SCHKS-SSVM

Posted on:2012-11-25Degree:MasterType:Thesis
Country:ChinaCandidate:L D WangFull Text:PDF
GTID:2218330368482954Subject:Communication and Information System
Abstract/Summary:PDF Full Text Request
With the diversification and the complication of communication system and signal modulation pattern, the wireless communication environment is more and more complicated, the communication signals overlap in the time domain,the frequency domain and the airspace, it makes signal recognition more difficult and makes the research of the communication signals modulation identification become attention topic in civil and military field. Referencing to the existing knowledge, this paper puts forward a new identification method based on the deformation smooth support vector machine. Simulation proves that this method can recognize the modulation mode of hybrid communication signals.First, this paper analyses the modulation principle of seven common digital signals. Given the study of the usual feature extraction and the carrier frequency estimation, choose six characteristics to be recognition characteristic vector. And then, according to the receive signal for aliasing signal,prior knowledge less,separation difficult and so on, introducing to independent component analysis method. It separates the independent composition from the observed signals which are mixed by several independent sources. Finally, researchs the theory about the support vector machine (SVM). The second order objective function of traditional support vector machine quadratic programming problem is not smooth,not micro. So it introduces a smooth deformation support vector machine (SCHKS-SSVM) mode, which uses CHKS function as smooth function. The model is trained by Newton-Armijo algorithm. Beacause it can improves the training speed through the batch train, meanwhile saving a lot of storage space. Experiments show that the proposed method can effectively solve high-dimension,large-scale classification problem.
Keywords/Search Tags:modulation mode identification, Independent component analysis, Support vector machine, Smooth functions
PDF Full Text Request
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