A Robust Beamforming Algorithm in the Situation of Limited Snapshots

To solve the problem of deviation of mainlobe and high sidelobe in the adaptive beamformer in the situation of limited snapshots, this paper presents a robust beamforming algorithm in the situation of limited snapshots. The proposed algorithm amends the exponent of covariance matrix of input signal in the Linearly Constrained Minimum Variance adaptive(LCMV) beamformer which is computed by Sample Matrix Inversion(SMI) algorithm to make the small eigenvalues of the covariance matrix close to each other in the situation of limited snapshots. This algorithm can diminish the deviation of mainlobe and suppress the high sidelobe. By analyzing the signal to interference plus noise ratio of the proposed algorithm, this paper gives the range of the exponent of covariance matrix. Simulation results demonstrate the validity and superiority of the proposed algorithm.