Stochastic Modeling for Static GPS Baseline Data Processing

Abstract

In global positioning system (GPS) data processing, incorrect stochastic models for double-differenced measurements will result in unreliable statistics for ambiguity search and biased positioning results. In the commonly used stochastic model, it is usually assumed that all the raw GPS measurements are independent and that they have the same variance. In fact, these assumptions are not realistic. Measurements obtained from different satellites cannot have the same accuracy due to varying noise levels. In this paper, a new method based on modern statistical theory is proposed to directly estimate the covariance matrix for double-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 be reduced and the reliability of the ambiguity resolution is improved. Also, the statistics of the baseline components estimated with the proposed stochastic models are more efficient.