GNSS Navigation In Difficult Environments: Hybridization And Reliability

Satellite navigation is critical in signal-degraded environments where GNSS signals are strongly corrupted. In these cases the use of a single GNSS does not guarantee an accurate and continuous positioning. A possible approach to solve this problem is the use of multi-constellation that provides additional measurements improving the redundancy of the system. Measurements in urban scenario
are strongly affected by gross errors, degrading navigation solution; hence a quality check on redundant measurements, defined as RAIM is necessary. The classical RAIM algorithms, developed for aviation, need to be redesigned for urban applications, considering frequent multiple blunders. The FDE schemes analyzed in this research are the Subset, the Forward-Backward and the Danish; they are obtained by combining different basic statistical tests. Specifically a so-called Global Test is adopted to verify the measurement self-consistency. A Local Test is used to identify and reject a blunder into a data set declared not-consistent. The considered FDE methods are modified to optimize their behavior in urban scenario. Specifically a preliminary check based on the WARP parameter, generalization of the classical ARP, is implemented to screen out bad geometries. Moreover a large blunder could cause multiple
test failures, inducing incorrect measurements exclusions; hence a separability index is implemented to avoid the incorrect exclusion of blunder-free measurements. Development and testing RAIM algorithms for multi-constellation in multiple blunder case is a main target of this work. For indoor navigation pseudolites are adopted and different positioning algorithms are developed. Indoor positioning has
been demonstrated with meter level of accuracy.