Closely Spaced Multipath Mitigation in GNSS Receiver Based on Maximum Likelihood Estimation

Multipath is the dominant source of positioning error in modern GNSS receiver. Maximum likelihood (ML) parameter estimation is an optimal method to mitigate the multipath effects while ML involves nonlinear optimization and requires iterative algorithms. Iterative methods usually lack of global convergence when the paths are closely spaced, if the initial value is arbitrarily assigned. In this paper, however, we first employ a grid search method to choose the initial value before iteration. Most computation of the grid search can be done offline. After that, an iterative method with simple forms is used to improve the parameter accuracy and global convergence can be achieved with just a few iterations. The simulations results show the estimator of time delay is almost unbiased when the time relative delay of two paths is larger than 0.20 chips.