Journals: ML Estimator and Hybrid Beamformer for Multipath and Interference Mitigation in GNSS Receivers
G. Seco, J. Fernández Rubio and C. Fernandez Prades


This paper addresses the estimation of the code-phase (pseudorange) and the carrier-phase of the direct signal received from a direct-sequence spread-spectrum satellite transmitter. The signal is received by an antenna array in a scenario with interference and multipath propagation. These two effects are generally the limiting error sources in most high-precision positioning applications. A new estimator of the code- and carrier-phases is derived by using a simplified signal model and the maximum likelihood (ML) principle. The simplified model consists essentially of gathering all signals, except for the direct one, in a component with unknown spatial correlation. The estimator exploits the knowledge of the direction-of-arrival of the direct signal and is much simpler than other estimators derived under more detailed signal models. Moreover, we present an iterative algorithm, that is adequate for a practical implementation and explores an interesting link between the ML estimator and a hybrid beamformer. The mean squared error and bias of the new estimator are computed for a number of scenarios and compared with those of other methods. The presented estimator and the hybrid beamforming outperform the existing techniques of comparable complexity and attains, in many situations, the Cramér

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