A Maximum-Likelihood Synchronization Scheme for GPS Positioning in Multipath, Interference, and Weak Signal Environments

In this paper, a novel multipath parameters estimation approach for GPS receivers using the maximum likelihood (ML) principle is proposed. Exploiting the replication property of the GPS C/A code within each symbol, we develop a coherent preprocessing approach based on a sample mean model. This model employs long integration time that improves the SNR and enhances receiver robustness against interference and weak signal effects. It also allows a simplified expression for the likelihood function which facilitates the use of computationally efficient iterative techniques for solving the complex ML optimization problem. In the proposed approach, the ML estimator is iteratively computed using the fast and low-complexity SAGE (Space-Alternating Generalized Expectation Maximization) algorithm. The proposed scheme is a candidate for indoor GPS navigation, as demonstrated by computer simulations.