Rational signal subspace approximation with applications to DOA estimation

We have developed an approach for approximating the signal and noise subspaces which avoid the costly eigendecomposition or SVD. These subspaces were approximated using rational and power-like methods applied to the sample covariance matrix. It is shown that MUSIC and minimum norm frequency estimators can be derived using these approximated subspaces. These approximate estimators are shown to be robust against noise and overestimation of number of sources. A substantial computational saving would be gained compared with those associated with the eigendecomposition-based methods. Simulations results show that these approximated estimators have comparable performance at low signal-to-noise ratio (SNR) to their standard counterparts and are robust against overestimating the number of impinging signals