STOCHASTIC MODELING FOR STATIC GPS BASE-LINE DATA-PROCESSING

In global positioning system (GPS) data processing, incorrect stochast ic models for double-differenced measurements will result in unreliabl e statistics for ambiguity search and biased positioning results. In t he commonly used stochastic model, it is usually assumed that all the raw GPS measurements are independent and that they have the same varia nce. In fact,these assumptions are not realistic. Measurements obtaine d from different satellites cannot have the same accuracy due to varyi ng noise levels. In this paper, a new method based on modern statistic al theory is proposed to directly estimate the covariance matrix for d ouble-differenced GPS measurements. Three different stochastic models have been tested and analyzed. Test results indicate that by using the proposed stochastic models the volume of ambiguity search space can b e reduced and the reliability of the ambiguity resolution is improved. Also, the statistics of the baseline components estimated with the pr oposed stochastic models are more efficient.