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In this paper, we consider the problem of automatic modulation classificationwith multiple sensors in the presence of unknown time offset, phase offset andreceived signal amplitude. We develop a novel hybrid maximum likelihood (HML)classification scheme based on a generalized expectation maximization (GEM)algorithm. GEM is capable of finding ML estimates numerically that areextremely hard to obtain otherwise. Assuming a good initialization technique isavailable for GEM, we show that the classification performance can be greatlyimproved with multiple sensors compared to that with a single sensor,especially when the signal-to-noise ratio (SNR) is low. We further demonstratethe superior performance of our approach when simulated annealing (SA) withuniform as well as nonuniform grids is employed for initialization of GEM inlow SNR regions. The proposed GEM based approach employs only a small number ofsamples (in the order of hundreds) at a given sensor node to perform both timeand phase synchronization, signal power estimation, followed by modulationclassification. We provide simulation results to show the computationalefficiency and effectiveness of the proposed algorithm.

Asynchronous Linear Modulation Classification with Multiple Sensors via Generalized EM Algorithm