Performances analysis of the propagator method for source bearing estimation

This paper investigates the performances of the propagator method (PM) in terms of the mean squared error (MSE) on the bearing estimates and in terms of the computational complexity. The propagator method (PM) is a subspace-based method which does not require any eigendecomposition of the cross-spectral matrix (CSM) of the received signals or any singular value decomposition (SVD) of the data matrix. We show that the performance of the PM are more sensitive to the fact that the basis of the noise subspace is not orthonormal than to the presence of noise in the data. We therefore propose a new version of the PM referred to as the orthonormal propagator method (OPM). We show that at high and medium signal-to-noise ratio, the OPM performs like MUSIC with a complexity reduced by the number of sources to the number of sensors ratio. The OPM can also perform like MUSIC at low SNR when the assumed number of sources is slightly overdetermined