Norm-Constrained Capon Beamforming Using Multirank Signal Models With Kalman Filter Implementation

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The performance of the Capon beamforming is known to degrade dramatically in the presence of model mismatches, especially when the desired signal is present in the training data. To improve the robustness, the diagonal loading technique was introduced. However, the major drawback is the selection of the diagonal loading level, which is related to the unknown signal powers. Another way to alleviate performance degradation is to use a more accurate signal model of the array response. In this paper, a norm-constrained Capon beamforming using multirank signal models is proposed. Based on the pseudo-observation method, the quadratic constraints can be easily constructed. The problem is solved by a nonlinear Kalman filter which can be implemented on-line. The simulation results show that the design of the norm-constraint value is less sensitive to the signal powers, small angle mismatches, and number of sensors with a standard linear array. Further, it is shown that the use of a multirank signal model and Kalman filter technique result in less self-cancellation and performance degradation than that of the rank-1 signal model and the estimation of sample matrix.