The ionospheric effect is considered to be one of the most important error sources limiting the quality of GPS kinematic positioning. Over longer distances, differential ionospheric residuals become larger and may affect the ambiguity resolution process. We present here a Kalman-filter-based GPS ionosphere model for long-baseline kinematic applications. This observational model includes the differential ionosphere as an additional unknown factor with position coordinates and ambiguities, while the temporal correlations of the state vector are specified in the dynamic model. The temporal behaviour of ionospheric residuals is determined by the analysis of their autocorrelation function. This newly developed method was applied on a set of data collected by a roving receiver located offshore of Oran (Algeria). The results show that for baselines of about 80 km, the root mean square is at the level of a few centimetres. For tests of baselines of about 51 km, the comparison between short-and longbaseline solutions revealed that mean differences of a few millimetres and 2 cm are obtained for the horizontal coordinates and vertical component, respectively, and the standard deviation (σ ) of differences on the scale of a few centimetres.

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