THE PROPAGATOR METHOD FOR SOURCE BEARING ESTIMATION

This paper investigates the propagator method as a possible alternative to the MUSIC method for source bearing estimation with arrays consisting of a large number of sensors. Indeed, the propagator method (PM) is a subspace-based method which does not require the eigendecomposition of the cross-spectral matrix (CSM) of the received signals. The propagator is a linear operator which only depends on the steering vectors and which can be easily extracted from the data. We here propose a new version of the propagator method referred to as the orthonormal propagator method (OPM). The performance of the PM and the OPM is theoretically analysed in terms of the mean squared error on the source bearing estimates and in terms of computational complexity. The performance results are then compared to those of MUSIC. We find that at high and medium signal-to-noise ratio, the OPM performs quite like MUSIC with a complexity reduced by the ratio of the number of sources to the number of sensors. Simulations are presented to strengthen the theoretical results. At low signal-to-noise ratio, the OPM can also perform like MUSIC when the assumed number of sources is slightly overestimated.