HIGAIN

The project deals with the development of advanced technologies and algorithms that can support the goal of highly accurate and highly integrated satellite navigation and highly accurate indoor navigation.

Mobility applications such as air cabs, autonomous drones, autonomous cars, ships in ports and trains require accurate and reliable navigation solutions to shape the future of transportation on land, at sea and in the air in a safe way. Global Navigation Satellite Systems (GNSS) already provide accurate positioning and timing services to users around the world. However, the environments where accurate and reliable positioning is required also pose the greatest technical challenges to achieving the desired performance. For example, the poor visibility and shadowing of GNSS satellites in urban canyons and tunnels, radio frequency interference and multipath propagation in urban environments require navigation solutions that go beyond standard GNSS solutions. In addition, GNSS is only available outdoors. Indoor positioning of pedestrians and autonomous drones/robots with the above-mentioned accuracy and availability requires a completely different approach. Improvements for GNSS are currently being investigated both on the user side (i.e. GNSS receivers and signal processing) and on the system side (i.e. improving the GNSS system and processing architectures at space and ground segment level). To contribute to the further improvement of the system side of Galileo in the future, DLR has started projects (OTTEx-Pre, COMPASSO, GAUSS) and is planning further projects (Kepler-Vision) that aim to make a revolutionary, rather than evolutionary, change from the current GNS system architecture to the Kepler concept, the activities of which will be independently funded within the Kepler-Vision project. The Kepler-Vision and GAUSS projects are focused on the further development of the system side of future GNSS architectures (especially KEPLER). In contrast, HiGAIN focuses on the user side. The projects do not yet have consolidated funding. For this reason, smaller tasks that are necessary to ensure connectivity are carried out in HiGAIN. This applies in particular to the integrity concept. The data for this is to be provided in GAUSS and used in HiGAIN. There are also certain synergies in the provision of reliable time from disturbed and noisy measurements. This is relevant in applications as well as on the system side, i.e. in the time generation of Kepler. In the latter case, the noise of the optical measurements is included and this must be investigated as a priority, which is why it is addressed here.

In addition to DLR-SO, the following institutes are involved in the project:

• DLR-KN

Characterization of ionospheric threats such as gradient, rates and scintillations using the Experimental and Validation Network (EVNet). EVNet is a near real-time network based on modular configurable and customizable hardware and software components for monitoring ionospheric conditions. EVNet enables the detection and investigation of ionospheric disturbances to assess threats to satellite navigation.

The characterization of disturbances will contribute to this:

• Develop a new ionospheric broadcast model
• Determine an over-bounding model for residual ionospheric delays in the dual-frequency linear combination
• Develop threat detection algorithms and provide ground-based integrity using big data and machine learning techniques