Kalman-filter-based Gps Ambiguity Resolution for Real-time Long-baseline Kinematic Applications

Resolving the GPS carrier-phase ambiguities has been a continuing challenge for sub-centimeter-level high-precision GPS positioning. In kinematic and long-baseline applications, the challenge turns out to be even greater due to the substantial problems in the observation time series – the decorrelation of biases, the quasi-random behavior of multipath and the correct interpretation of receiver system noise for observations conducted in kinematic mode – which can be ignored to some degree in static and shortbaseline applications. As baseline lengths grow longer, eventually, these problems will make it difficult to get reliable ambiguity solutions in kinematic applications. We have found that the problems related to kinematic long-baseline applications can be handled in an optimal way when a particular generalized procedure is adopted in the observations processing scheme. The generalized procedure includes: a functional model which takes into account all significant biases; a stochastic model which is derived directly from the observation time series; a quality control scheme which handles cycle slips (or outliers); and a parameter-estimation scheme which includes a simultaneous ambiguity search process. The prototype approach described in this paper follows the generalized procedure. For each stage of the procedure, the new concepts of our approach are explained and some preliminary test results are given.