%0 Journal Article %1 141456 %A Soliman, S. S. %A Hsue, S. Z. %D 1992 %J Communications, IEEE Transactions on %K *file-import-12-03-02 algorithmmonotonic, analysis, based, bpskcnrm-ary, classification, classifierquasi-log-likelihood, classifiersignal, classifierstatistical, functionphase, hypothesis, increasing, keyingstatistical, momentspattern, noise, phasesquare-law, probabilitymodulation, processing, psk, ratio, ratiodecision, recognitionphase, rulegeneral, shift, signal, signalsqpskcarrier, testmisclassification, to, %N 5 %P 908--916 %R 10.1109/26.141456 %T Signal classification using statistical moments %U http://dx.doi.org/10.1109/26.141456 %V 40 %X An automatic modulation classification algorithm utilizing the statistical moments of the signal phase is developed and used to classify the modulation type of general M-ary PSK signals. It is shown that the nth moment (n even) of the phase of the signal is a monotonic increasing function of M. On the basis of this property, the authors formulate a general hypothesis test, develop a decision rule, and derive an analytic expression for the probability of misclassification. Two examples are given to demonstrate the performance of the algorithm. The algorithm is compared with the quasi-log-likelihood radio (qLLRC), square-law (SLC), and phase-based (PBC) classifiers. The algorithm is outperformed by q LLRC at low CNR but is comparable to SLC and is better than PBC. The qLLRC algorithm is only valid at CNR lt;0 dB and can be used only to discriminate between BPSK and QPSK signals, whereas the moments algorithm is more general

@article{141456, abstract = {{An automatic modulation classification algorithm utilizing the statistical moments of the signal phase is developed and used to classify the modulation type of general M-ary PSK signals. It is shown that the nth moment (n even) of the phase of the signal is a monotonic increasing function of M. On the basis of this property, the authors formulate a general hypothesis test, develop a decision rule, and derive an analytic expression for the probability of misclassification. Two examples are given to demonstrate the performance of the algorithm. The algorithm is compared with the quasi-log-likelihood radio (qLLRC), square-law (SLC), and phase-based (PBC) classifiers. The algorithm is outperformed by q LLRC at low CNR but is comparable to SLC and is better than PBC. The qLLRC algorithm is only valid at CNR lt;0 dB and can be used only to discriminate between BPSK and QPSK signals, whereas the moments algorithm is more general}}, added-at = {2012-03-02T03:39:18.000+0100}, author = {Soliman, S. S. and Hsue, S. Z.}, biburl = {https://www.bibsonomy.org/bibtex/231bb8d12b8fdff123e3286aeb12f2761/baby9992006}, citeulike-article-id = {10403167}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/26.141456}, doi = {10.1109/26.141456}, interhash = {4879ffef30fcc90052feaaed2be094ac}, intrahash = {31bb8d12b8fdff123e3286aeb12f2761}, journal = {Communications, IEEE Transactions on}, keywords = {*file-import-12-03-02 algorithmmonotonic, analysis, based, bpskcnrm-ary, classification, classifierquasi-log-likelihood, classifiersignal, classifierstatistical, functionphase, hypothesis, increasing, keyingstatistical, momentspattern, noise, phasesquare-law, probabilitymodulation, processing, psk, ratio, ratiodecision, recognitionphase, rulegeneral, shift, signal, signalsqpskcarrier, testmisclassification, to,}, month = may, number = 5, pages = {908--916}, posted-at = {2012-03-02 02:22:44}, priority = {2}, timestamp = {2012-03-02T03:39:18.000+0100}, title = {{Signal classification using statistical moments}}, url = {http://dx.doi.org/10.1109/26.141456}, volume = 40, year = 1992 }