An adaptive wavelet transform based on cross-validation and its application to mitigate GPS multipath effects

GPS multipath disturbance is a bottleneck problem that limits accuracy of Global Positioning System (GPS) positioning. A method based on the technique of cross-validation to automatically identify wavelet signal layers is developed for separating noise from signals in data series, and applied to mitigate GPS multipath effects. Experiments with both simulated data series and real GPS observations show that the method is a powerful signal decomposer, which can successfully separate noise from signals as long as the noise level is lower than about half of the magnitude of the signals. A multipath correction model can be derived with the proposed method and the sidereal day-to-day repeating property of GPS multipath signals can be used to remove multipath effects in subsequent days of GPS observations, and therefore improve the quality of the GPS results.