Accurate and reliable relative navigation is the prerequisite to guarantee the effectiveness and safety of various multiple Unmanned Aerial Vehicles (UAVs) cooperation tasks, when absolute position information is unavailable or inaccurate. Among the UAV navigation techniques, Global Navigation Satellite System (GNSS) is widely used due to its worldwide coverage and simplicity in relative navigation. However, the observations of GNSS are vulnerable to different kinds of faults arising from transmission degradation, ionospheric scintillations, multipath, spoofing, and many other factors. In an effort to improve the reliability of multi-UAV relative navigation, an autonomous integrity monitoring method is proposed with a fusion of double differenced GNSS pseudoranges and Ultra Wide Band (UWB) ranging units. Specifically, the proposed method is designed to detect and exclude the fault observations effectively through a consistency check algorithm in the relative positioning system of the UAVs. Additionally, the protection level for multi-UAV relative navigation is estimated to evaluate whether the performance meets the formation flight and collision avoidance requirements. Simulated experiments derived from the real data are designed to verify the effectiveness of the proposed method in autonomous integrity monitoring for multi-UAV relative navigation.

Autonomous Integrity Monitoring for Relative Navigation of Multiple Unmanned Aerial Vehicles

In this article, a new concept for SBAS integrity validation is presented. The proposed concept is a modification of the well known Stanford diagram [2], where a 2D histogram shows the relationship of position errors against protection levels for a set of measurements using an all in view satellite selection. The new method consists on two diagrams: the Worse-Safety Index diagram and the “All-Geometries” diagram, known here as the Stanford-ESA and the All-Stanford-ESA, respectively. The first consist on taking, at each sample time and given location, the worst possible satellite geometrical combination (out of all possible combinations) from a SBAS integrity margin viewpoint. In the second, all possible geometries are displayed and, in case of MIs, the geometries associated to each epoch are leveled with different symbols and colors. It allows, to easily identify the different clusters and to assess the time correlation of the events. Real measurement results are presented here showing that the EGNOS integrity margins remain safe under this very exigent criterion, a certainly very positive result. It is suggested here to use the Stanford-ESA Integrity concept, for routine performance monitoring and to support and complement the safety case of the EGNOS system with real experimental data.

The Stanford-ESA integrity diagram: focusing on SBAS integrity

This paper presents a novel Kalman filter (KF)-based receiver autonomous integrity monitoring (RAIM) algorithm for reliable aircraft positioning with global navigation satellite systems (GNSS). The presented method overcomes major limitations of the authors' previous work, and uses two GNSS, namely, Navigation with Indian Constellation (NavIC) of India and the Global Positioning System (GPS). The algorithm is developed in the range domain and compared with two existing approaches-one each for the weighted least squares navigation filter and KF. Extensive simulations were carried out for an unmanned aircraft flight path over the Indian sub-continent for validation of the new approach. Although both existing methods outperform the new one, the work is significant for the following reasons. KF is an integral part of advanced navigation systems that can address frequent loss of GNSS signals (e.g., vector tracking and multi-sensor integration). Developing KF RAIM algorithms is essential to ensuring their reliability. KF solution separation (or position domain) RAIM offers good performance at the cost of high computational load. Presented range domain KF RAIM, on the other hand, offers satisfactory performance to a certain extent, eliminating a major issue of growing position error bounds over time. It requires moderate computational resources, and hence, shows promise for real-time implementations in avionics. Simulation results also indicate that addition of NavIC alongside GPS can substantially improve RAIM performance, particularly in poor geometries.

https://www.mdpi.com/1424-8220/20/22/6606/pdf

Kalman Filter-Based RAIM for Reliable Aircraft Positioning with GPS and NavIC Constellations

Lack of direct line-of-sight (LOS) signals from mainstream Global Navigation Satellite System (GNSS) in deep urban canyon environments leads to poor Positioning, Navigation and Timing (PNT) accuracy. In spite of various available techniques and solutions, the PNT accuracy is not even comparable with non-urban canyon environment. With an emergence of new applications such as autonomous driving and indoor positioning, and considering the aforementioned weakness of GNSS this research analyzes an alternative solution using Low Earth Orbit (LEO) Mega Constellations for PNT estimation. This paper compares the positioning performance in terms of Geometric Dilution of Precision (GDOP) of mainstream GNSS (GPS, Galileo) and LEO Mega Constellations (Starlink, OneWeb) by designing a Deep Urban Canyon Environment. The paper demonstrates that LEO constellations offers better GDOP due to more visible satellites at a given epoch in urban canyon with respect to GNSS.

Positioning Performance of LEO Mega Constellations in Deep Urban Canyon Environments

Lack of direct line-of-sight (LOS) signals from mainstream Global Navigation Satellite System (GNSS) in deep urban canyon environments leads to poor Positioning, Navigation and Timing (PNT) accuracy. In spite of various available techniques and solutions, the PNT accuracy is not even comparable with non-urban canyon environment. With an emergence of new applications such as autonomous driving and indoor positioning, and considering the aforementioned weakness of GNSS this research analyzes an alternative solution using Low Earth Orbit (LEO) Mega Constellations for PNT estimation. This paper compares the positioning performance in terms of Geometric Dilution of Precision (GDOP) of mainstream GNSS (GPS, Galileo) and LEO Mega Constellations (Starlink, OneWeb) by designing a Deep Urban Canyon Environment. The paper demonstrates that LEO constellations offers better GDOP due to more visible satellites at a given epoch in urban canyon with respect to GNSS. 

https://www.mdpi.com/2218-6581/9/3/66/pdf

Integrity analysis for GPS-based navigation of UAVs in urban environment

As the primary navigation source, GNSS performance monitoring and prediction have critical importance for the success of mission-critical urban air mobility and cargo applications. In this paper, a novel machine learning based performance prediction algorithm is suggested considering environment recognition. Valid environmental parameters that support recognition and prediction stages are introduced, and K-Nearest Neighbour, Support Vector Regression and Random Forest algorithms are tested based on their prediction performance with using these environmental parameters. Performance prediction results and parameter importances are analyzed based on three types of urban environments (suburban, urban and urban-canyon) with the synthetic data generated by a high quality GNSS simulator.

A Machine Learning Based GNSS Performance Prediction for Urban Air Mobility Using Environment Recognition

As a result of the increasing usage of UAVs (Unmanned Air Vehicles) in urban environments for UAM (Urban Air Mobility) applications, the preciseness and reliability of PNT (Positioning, Navigation and Timing) systems have critical importance for mission safety and success. With its high accuracy and global coverage, GNSS (Global Navigation Satellite System) is the primary PNT source for UAM applications. However, GNSS is highly vulnerable to Non-Line-of-Sight (NLoS) blockages and multipath (MP) reflections, which are quite common, especially in urban areas. This study proposes a machine learning-based NLoS/MP detection and exclusion algorithm using GNSS observables to enhance position estimations at the receiver level. By using the ensemble machine learning algorithm with the proposed method, overall 93.2% NLoS/MP detection accuracy was obtained, and 29.8% accuracy enhancement was achieved by excluding these detected signals. 

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Performance Enhancement of Low-Cost INS/GNSS Navigation System Operating in Urban Environments

/pdf/uav-path-planning-optimization-based-on-gnss-quality-and-2p3fiq2l59.pdf

Oguz Kagan Isik

Papers

Integrity analysis for GPS-based navigation of UAVs in urban environment

A Machine Learning Based GNSS Performance Prediction for Urban Air Mobility Using Environment Recognition

Performance Enhancement of Low-Cost INS/GNSS Navigation System Operating in Urban Environments

UAV path planning optimization based on GNSS quality and mission requirements