In high-precision global navigation satellite system applications, it is often not possible to simultaneously meet the requirements for fast and reliable integer ambiguity resolution. For a given reliability constraint in form of a user-defined, tolerable probability of an incorrect ambiguity estimate, resolving a subset of ambiguities instead of the full set can be beneficial. We discuss a fixed failure rate implementation of a data-driven, likelihood-ratio-based partial ambiguity resolution technique. A key problem in this context is the efficient determination of a scalar that is a model-dependent threshold value. This problem is approached via a conservative functional approximation of the threshold value. The only input parameter of the function is the integer least-squares failure rate of the system model under consideration. Numerically simulated single and combined system GPS/Galileo single baseline cases with single- and dual-frequency measurements are used to analyze the impact of the approximation. The results indicate that the conservative description hardly affects the performance of the algorithm, while the predefined failure rate is not exceeded. Moreover, it is shown that the presented data-driven partial ambiguity resolution approach clearly outperforms a purely model-driven scheme based on the bootstrapping failure rate.     «

In high-precision global navigation satellite system applications, it is often not possible to simultaneously meet the requirements for fast and reliable integer ambiguity resolution. For a given reliability constraint in form of a user-defined, tolerable probability of an incorrect ambiguity estimate, resolving a subset of ambiguities instead of the full set can be beneficial. We discuss a fixed failure rate implementation of a data-driven, likelihood-ratio-based partial ambiguity resolution te...     »