A Correlation Coefficient Estimation Algorithm Based on Subspace Decomposition

Abstract

the correlation coefficient estimation algorithm based on subspace decomposition is presented in this paper. The correlation coefficient between the signals is obtained by getting eigen-value decomposition of the data covariance matrix, and deriving the relationship between signal subspace and noise subspace. Simulation results verify that this algorithm can be realized to get the correlation coefficient between two incident signals whose DOA are known and the effect of the correlation coefficient estimation is made by different signal direction angular intervals.