A parameterized maximum likelihood method for multipaths channels estimation

Abstract

In paths localization, a resolution that goes beyond the classical Rayleigh beamwidth is of great interest. To improve the resolution, model based techniques have been introduced (high resolution methods), but they are very sensitive to noise correlation and they assume underlying data model. We develop a parameterized maximum likelihood (PML) technique, based on a knowledge of the transmitted signal. We develop the exact PML approach and present its implementation by a Gauss Newton procedure. Simulations on data sets are examined. The performances are compared to the Cramer Rao bound. Its superiority over the traditional matched filter (MF) and the conditional maximum likelihood (CML) is shown. The paper concludes with the improvements introduced by a knowledge of the transmitted signals.