Maximum Likelihood Optimization of Adaptive Asynchronous Interference Mitigation Beamformer

In asynchronous (intermittent) interference scenarios, the content of co-channel sources over data interval may be different from interferers training interval, typically with extra presented interval. Under such conditions, conventional adaptive beamformer designed lose its efficiency when applied to this paper, we address problem by 1) formulating a family second order statistics beamformers regularized covariance matrix estimated interval; 2) proposing maximum likelihood methodology for optimization combined (mixed) based on maximization product ratio that checks accuracy recovered signals and equality eigenvalues in complementary signal subspace defined 3) demonstrating robustness proposed solution as linear an initialization iterative projections finite alphabet scenarios comparing basic rejection combining receivers.