RAIM algorithms for aided GNSS in Urban scenario

Urban canyon is a critical scenario for satellite navigation, because many GNSS signals are blocked by artificial obstacles or severely degraded; in standalone mode GPS, currently the main GNSS, cannot guarantee an accurate and continuous positioning. A possible approach to overcome these limitations is the use of multiple GNSS systems. GLONASS, the Russian navigation satellite system, is currently fully operational and is the main candidate to support this thesis. Urban scenario is mainly affected by multipath phenomenon, yielding several blunders into the measurements and unacceptable errors in the navigation solution. The integrity concept was introduced for safety-of-life application as aviation to provide timely warnings to users when a system should not be used for navigation, and then it was expanded to not safety-of-life service as urban navigation. RAIM (Receiver Autonomous Integrity Monitoring) techniques are user-level integrity methods based on consistency check of redundant measurements. This check is crucial because only at user-level certain local errors, such as multipath and local interferences, can be detected. Multi-constellation GNSS improves navigation solution in terms of accuracy and continuity; a further enhancement is achievable even in terms of integrity owing to the gained redundancy. The multiconstellation use implies a further unknown related to the intersystem time scale offset, requiring the " sacrifice" of one measurement. This parameter is observed to be quasi-constant in the short term, so an aiding can be introduced to account for its behavior. A similar approach can be adopted for altitude considering its slow variations in urban scenario. In this work GPS/GLONASS systems are combined and the benefits of the aforesaid aids are assessed, with main focus being the improvements in terms of integrity; single point GNSS and snapshot RAIM algorithms are herein considered. PVT and RAIM algorithms are developed in MatLab®environment and belong to a tool implemented by PANG (PArthenope Navigation Group). © 2012 IEEE.