Unconstrained Robust Adaptive Beamforming with Improved Capon Estimator

In this paper, an improved Capon estimator is introduced, and reserves the characteristics of signal space after filtering the noise. More precise power estimation for both noise and signal from different directions can be obtained. Then, the covariance matrices of both interference-plus-noise and signal are reconstructed. With the estimated noise power and the covariance matrices, we propose 4 similar algorithms. Different from the previous methods, the proposed algorithms avoid the robust conditions in the constraints, even to solve a new convex optimization problem which would increase the amount of computation significantly. Simulation results show that the improved Capon estimator achieves better spatial resolution than existing methods, and the performance of the proposed robust adaptive beamforming is almost close to the optimal value across a wide range of signal to interference and noise ratio.