Role of stochastic model on GPS integer ambiguity resolution success rate

An important step in the high-precision GPS positioning is double-difference integer ambiguity resolution (IAR). The fraction or percentage of success among a number of integer ambiguity fixing is called the success rate. We investigate the ambiguity resolution success rate for the GPS observations for two cases, namely a nominal and a realistic stochastic model of the GPS observables. In principle, one would expect to have higher reliability on IAR success rates if a realistic GPS observables stochastic model is employed. The GPS geometry-based observation model is employed in which a more realistic stochastic model of GPS observables is determined using the least-squares variance component estimation. Two short and one GPS long baseline datasets and one simulated dataset are employed to evaluate the efficacy of the proposed algorithm. The results confirm that a more realistic stochastic model can significantly improve the IAR success rate on individual frequencies, either on L1 or on L2. An improvement of 25 % was achieved to the empirical success rate results. The results are of interest for many applications in which single-frequency observations can be used. This includes applications like attitude determination using single frequency single epoch of GPS observations.