Residual Error Analysis of GPS Data Sequence Based on WP

GPS observation sequence contains all kinds of impact factors and the function relations between them are complicated. That influences the extraction of feature information and the ability of explanation of parameter models. In residual error that still exists in the signal after a series of professional treatment, including difference correcting, tide correcting, and so on, system error has a relative high value compared to random error. Further more, information of different factors impacting observations behaves different in system error, and shows some periodicity in the frequency domain. If these factors can be departed during positioning and orbit determination, not only will the precision of the two be enhanced but also that can provide materials for the study of other disciplines. Traditional parameter estimation methods in these issues don't appear so efficiency. In this paper, periodicity of error of GPS data sequence is analyzed, and residual error items with periods of a year, half of a year, a month and half of a month are extracted within different frequency bands obtained by wavelet packet transform. Then the corresponding residual errors are acquired. Frequency aliasing between sub-bands appears during the above-mentioned process. Wavelet filters applied aren't ideal so that one sub-band obtains some frequencies belong to other near ones and up-sampling and down-sampling these mixed sub-bands will cause frequency folding, for not satisfying the sampling theorem. Both of them lead to the frequency aliasing during decomposition and reconstruction of wavelet packet algorithm. To Eliminate or weaken the impact of aliasing, relevant measures are studied to improve period items quality of GPS data. The availability of method is proved in testing example, and items with period of a year, half of a year, a month and half of a month acquired with it, are more believable.