Semi-Blind Adaptive Beamforming for Cyclostationary Signals: A Kalman Filtering Approach

In this paper, we develop a new adaptive beamforming algorithm for cyclostationary signals. Our algorithm is derived by maximizing the cyclic moment of the beamformer´s output subject to a constraint that preserves all the signals within a prescribed uncertainty set. This constraint allows the beam-former to capture the desired signal and suppress any cyclostationary interferers using the (possibly erroneous) prior information about the array manifold. We develop a state-space model for the underlying optimization problem and derive an iterative cyclic beamforming algorithm using the second-order extended Kalman filter (EKF). Numerical simulations are presented showing the superior performance of our beam-former compared to earlier cyclic beamforming techniques.