A data driven partial ambiguity resolution: Two step success rate criterion, and its simulation demonstration

Ambiguity Resolution (AR) is a key technique in GNSS precise positioning. In case of weak models (i.e., low precision of data), however, the success rate of AR may be low, which may consequently introduce large errors to the baseline solution in cases of wrong fixing. Partial Ambiguity Resolution (PAR) is therefore proposed such that the baseline precision can be improved by fixing only a subset of ambiguities with high success rate. This contribution proposes a new PAR strategy, allowing to select the subset such that the expected precision gain is maximized among a set of pre-selected subsets, while at the same time the failure rate is controlled. These pre-selected subsets are supposed to obtain the highest success rate among those with the same subset size. The strategy is called Two-step Success Rate Criterion (TSRC) as it will first try to fix a relatively large subset with the fixed failure rate ratio test (FFRT) to decide on acceptance or rejection. In case of rejection, a smaller subset will be fixed and validated by the ratio test so as to fulfill the overall failure rate criterion. It is shown how the method can be practically used, without introducing a large additional computation effort. And more importantly, how it can improve (or at least not deteriorate) the availability in terms of baseline precision comparing to classical Success Rate Criterion (SRC) PAR strategy, based on a simulation validation. In the simulation validation, significant improvements are obtained for single-GNSS on short baselines with dual-frequency observations. For dual-constellation GNSS, the improvement for single-frequency observations on short baselines is very significant, on average 68%. For the medium- to long baselines, with dual-constellation GNSS the average improvement is around 20–30%.