July 17th, 2025
ESA and Neuraspace work to minimize signal noise through GNSS advances

Neuraspace is working with the European Space Agency (ESA) to use innovative GNSS technologies to minimize signal noise under a new NAVISP project. Neuraspace is an expert in space domain awareness (SDA) solutions,

“Stop Getting Noise – Automated GNSS Processing for Smarter Orbits” (NAVISP Element 2) seeks to address critical operational challenges faced by commercial satellite operators, launch service providers and defense and government agencies.

Challenges to be addressed include the urgent need for more scalable, accurate and autonomous orbit determination, particularly for satellite mega-constellations, in an increasingly congested space environment. While defense and government agencies demand high-confidence SDA solutions amid increasing geopolitical tensions, satellite operators require reliable orbit tracking and early mission support.

The result is expected to use innovative GNSS technologies to reduce the risk of satellite collisions and enable satellite operators to make faster and more accurate decisions about safekeeping their assets. Solutions will also lead to more efficient operations with lesser reliance on ground infrastructure and smarter fuel management translating into lower mission costs.

In particular, the project includes:

• GNSS Data Cleanup to remove biases and noise to improve the precision of orbit determination.
• GNSS Orbital Phase Correction by introducing lightweight onboard algorithms designed to run on resource-constrained satellite systems. The algorithms will use real-time data to enable satellites to autonomously correct trajectory predictions and minimize reliance on ground stations, saving time and resources.
• GNSS Orbit Determination Accuracy to provide better orbit predictions by developing advanced methodologies to deliver critical positioning information for safe operations and maneuver planning.

ESA and Neuraspace work to minimize signal noise through GNSS advances – GPS World