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In this thesis we present a modulation classifier for classifying the M-ary frequency-shifted keying (MFSK) signal corrupted by the additive white Gaussian noise. There are two stages during the development of the classification algorithm. At the first stage we extract the instantaneous frequency from raw data which is generated by the computer simulation program. Next the histogram is drawn by using the instantaneous frequency samples. It is found that the resulting histogram can be viewed or approximated to be the Tikhonov function in which the parameter must be modified. The omdified Tikhonov functions are used to be the probability densities for M-ary FSK signals and will be the key role for deriving the required test statistics in the second stage. In the second stage we develop the test statistics, then employ the derived test statistics to construct an MFSK modulation classifier. We give an example to demonstrate the capability of BFSK/4FSK classifier in term of the probability of successful classification. The results via computer simulations show that successful rate of the proposed classifier is higher than 99.8% when SNR>10dB.
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