Stochastic modeling of triple-frequency BeiDou signals: estimation, assessment and impact analysis

Abstract

Stochastic models are important in global navigation satellite systems (GNSS) estimation problems. One can achieve reliable ambiguity resolution and precise positioning only by use of a suitable stochastic model. The BeiDou system has received increased research focus, but based only on empirical stochastic models from the knowledge of GPS. In this paper, we will systematically study the estimation, assessment and impacts of a triple-frequency BeiDou stochastic model. In our estimation problem, a single-difference, geometry-free functional model is used to extract pure random noise. A very sophisticated structure of unknown variance matrix is designed to allow the estimation of satellite-specific variances, cross correlations between two arbitrary frequencies, as well as the time correlations for phase and code observations per frequency. In assessing the stochastic models, six data sets with four brands of BeiDou receivers on short and zero-length baselines are processed, and the results are compared. In impact analysis of stochastic model, the performance of integer ambiguity resolution and positioning are numerically demonstrated using a realistic stochastic model. The results from ultrashort (shorter than 10 m) and zero-length baselines indicate that BeiDou stochastic models are affected by both observation and receiver brands. The observation variances have been modeled by an elevation-dependent function, but the modeling errors for geostationary earth orbit (GEO) satellites are larger than for inclined geosynchronous satellite orbit (IGSO) and medium earth orbit (MEO) satellites. The stochastic model is governed by both the internal errors of the receiver and external errors at the site. Different receivers have different capabilities for resisting external errors. A realistic stochastic model is very important for achieving ambiguity resolution with a high success rate and small false alarm and for determining realistic variances for position estimates. To the best of our knowledge, this paper is the first comprehensive study on such stochastic models used specifically with BeiDou data.