Modified Eigenvector Projection Approach for Subspace Estimation Adaptive Beamforming

摘要

This paper introduces a Modified Eigenvector Projection approach for Subspace estimation (MEPS), which is robust and has high convergence rate. First, the KR signal subspace is exploited to estimate the covariance matrix. Then, interference plus noise subspace is constructed. And the desired signal steering vector is projected to this subspace. MEPS can estimate the sample covariance matrix more precisely without knowing the number of the signal sources. Simulation results show that MEPS can make convergence in limited snapshots and maintain a high Signal to Interference Plus Noise ratio (SINR) in a large range of input Signal to Interference Rate (SNR). Besides, without knowing the number of the signal sources, this method can estimate the sample covariance matrix more precisely.