Fast algorithm for minimum-norm direction-of-arrival estimation
В. Т. Ермолаев, Alex B. Gershman · IEEE Transactions on Signal Processing · 1994
The original minimum-norm direction-of-arrival estimator, proposed by Kumaresan and Tufts, employs the noise-subspace projection matrix, calculated by the eigendecomposition of spatial covariance matrix. The present authors propose a novel noneigenvector fast algorithm, which calculates the required minimum-norm function using the special power basis instead of eigenvector basis. The proposed algorithm provides a substantial saving as compared with computational loads of the eigendecomposition-based minimum-norm algorithm in cases when the number of multiple sources is much lower than the number of array sensors. Some computer simulation results, verifying the high performance and accuracy of the proposed algorithm, are presented.>