  Blind algorithms for multiple-input multiple-output (MIMO) signals
interception have recently received considerable attention because of their
important applications in modern civil and military communication fields. One
key step in the interception process is to blindly recognize the modulation
type of the MIMO signals. This can be performed by employing a Modulation
Classification (MC) algorithm, which can be feature-based or likelihood-based.
To overcome the problems associated with the existing likelihood-based MC
algorithms, a new algorithm is developed in this paper. We formulated the MC
problem as maximizing a global likelihood function formed by combining the
likelihood functions for the estimated transmitted signals, where Minimum Mean
Square Error (MMSE) filtering is employed to separate the MIMO channel into
several sub-channels. Simulation results showed that the proposed algorithm
works well under various operating conditions, and performs close to the
performance upper bound with reasonable complexity.
