# Integrity Analysis for GPS-Based Navigation of UAVs in Urban Environment
## 1 Introduction
While alternative PNT solutions have specific advantages in particular environments and missions, GNSS, however, remains the main navigation source (when it is available) as it provides the best positioning and timing in most of the cases. Hence, monitoring of GNSS performance is one of the critical tasks for future integrated multi-sensor navigation systems, which allows one to estimate and maintain the integrity of GNSS-based navigation and to make appropriate decisions, e.g., concerning required alternative sensors, fusion algorithm parameters or augmentation solutions. Figure 1: Navigation Performance Criteria. The most common integrity monitoring approaches, which are currently in use are Satellite Autonomous Integrity Monitoring (SAIM), GNSS Integrity Channel (GIC) and Receiver Autonomous Integrity Monitoring (RAIM) [12], where SAIM focuses only on satellite and orbital errors, GIC takes into account the environmental errors sourced by transmission environment and RAIM monitors receive errors in addition to other errors from satellites and transmission paths. The range and position error models are characterized in open-sky conditions in these integrity monitoring approaches; therefore, integrity monitoring quality is highly degraded in urban environments because of the impact of multipath and satellite obscuration. The following challenges exist for integrity monitoring in urban air mobility applications [13]:  
* Integrity monitoring algorithms require a large amount of data for integrity checks, but the amount of available data dramatically decreases in urban environments because of limited satellite visibility. * Assumption of a single fault, which is frequently required for existing integrity monitoring solutions, is no longer valid for urban environments due to multipath and line-of-sight issues. Integrity measures, once available, could be used in multiple ways, e.g. for sensor switching or performance prediction. For example, authors in [14] implemented a rule-based sensor switching technique based on the integrity flags generated for each of the considered PNT sources. Another way to use integrity information is presented in [15] with a performance prediction by a Bayesian network based on the integrity failure rates of error sources. It is known [16] that dilution of precision (DOP) coefficients are among the biggest contributors to the positioning accuracy and, therefore, are linked to the integrity of the whole GNSS-based navigation system. Usage of DOP fields in addition to the conventional potential field method is suggested in [17] as one of the most recent and prospective examples of UAV path planning in known urban areas. Geometric DOP and the number of visible satellites were simulated for two different constellations, GPS and Galileo, and three different cut-off elevations at a specific time and position in [18]. Work [19] also provides a distribution of DOP coefficients and the number of visible satellites for GPS, Galileo, GLONASS and BeiDou under two different elevation levels. The satellite visibility and positioning accuracy under different sky view conditions are analyzed in [20] for GPS and GLONASS. Another work in the Asia-Australia region provides an ambiguity DOP and positioning accuracy analysis combining GPS and Galileo constellations and regional navigation systems, IRNSS and QZSS [21]. All these works analyze how DOP varies in time when different constellations are considered and under different environmental conditions. However, analysis of the environmental parameters, such as cut-off elevation and satellite visibility, for their joint use for integrity monitoring purposes received little attention in the publications known to authors.