Two-step success rate criterion strategy: a model- and data-driven partial ambiguity resolution method for medium-long baselines RTK

Abstract

AbstractWhen GNSS measurement errors such as ionospheric delays remain large, full ambiguity resolution (FAR) takes an unacceptably long time to fix ambiguities to integers. Partial ambiguity resolution (PAR), under this circumstance, is a possible solution to obtain precise positioning before FAR is achieved. PAR fixes a subset of ambiguities instead of all to improve either the fix rate, success rate or positioning accuracy according to different criteria. This contribution proposes a two-step success rate criterion (TSRC) strategy that first chooses ambiguities to fix using a given success rate threshold and then adds more ambiguities to fix by maximizing the expectation of baseline precision improvement from fixing ambiguities. Then, the TSRC strategy is compared with two other commonly used PAR strategies and the FAR strategy in an experiment with real data forming a medium-long-baseline setup (baselines longer than 15 km and shorter than 50 km). The results show that in medium-long-baseline cases, the TSRC achieves the shortest time to first fix (TTFF), which is 100–200 s shorter than other PAR strategies and 400–800 s shorter than the FAR strategy, excluding cases in which FAR fixes no ambiguities at all. Consequently, the TSRC yields the highest positioning accuracy on average. In addition, the variance–covariance (VC)-matrix of the float ambiguities is found to have a heavy impact on the TSRC strategy in some cases, and amplifying the VC-matrix before the ambiguity fixing process can partly mitigate it.