Adaptive Array Beamforming Using Signal Cyclostationarity and Finite Data

This paper considers adaptive array beamforming using signal cyclostationarity. Due to the effect of using finite data samples, there exists an estimation error in computing the weight vector required by performing cyclic beamforming. To deal with this problem, we formulate a cost function consisting of a posteriori information of the received signal and a priori information regarding the probabilistic distribution of the error. By minimizing the cost function, we obtain a weight vector with a diagonal loading data covariance matrix under a white Gaussian estimation error. An analytical solution for determining the loading factor is further derived. Simulation results for showing the effectiveness of the proposed method are provided.