Maximum Likelihood Modulation Recognition for Continuous Phase Modulation Signals Using Memory Factor
WU Bin YUAN Yabo WANG Bo
(Beijing Institute of Tracking and Telecommunication Technology, Beijing 100094, China)
(Key Laboratory of Space Object Measurement, Beijing 100094, China)
In order to solve the problem of low recognition accuracy of Continuous Phase Modulation (CPM) which is non-linear and with memory, a new maximum likelihood modulation recognition approach using memory factor is proposed in this paper. The approach defines the mapping symbol which has the time-homogeneous Markov property and generates the memory factor by calculating the posterior probability of the mapping symbol. Then, combining with the CPM decomposition and the EM algorithm, the time separable and channel parameter estimable likelihood function is deduced for CPM signals. The proposed approach has characters of low required symbol number, wide range of applicable SNR, large variety of recognizable CPM signals, high recognition accuracy and strong robustness to phase error. Simulation results show that the recognition rate of 8 kinds of CPM signals can reach more than 95% when the symbol number is 200, SNR is 0 dB and the phase error is arbitrary.
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