Technology Focus 1
Embedded AI & Deep Learning for Radar and GNSS Systems
As aerospace systems face an ever-growing volume of signals — often jammed, sometimes hostile — the ability to process and interpret data in real time has become a critical capability.
At ARKANE, we embed reconfigurable hybrid AI solutions directly into our systems to:
- maximize processing speed
- reduce resource consumption
A Hybrid Architecture for On-Board Decision-Making
We design hybrid architectures that combine traditional signal processing with neural networks optimized for embedded deployment. The goal is straightforward: enabling our systems to process and interpret complex signals directly at the source.
In practice, this means:
- Real-time operation, even in resource-constrained environments
- Significantly reduced processing latency
- Low power consumption and more efficient resource management
- Dynamic adaptability to changing signal or environmental conditions
Concrete Use Cases
In radar systems:
Our technology detects, tracks, and classifies targets in jammed or cluttered environments. Embedded AI improves resolution, strengthens tracking reliability, and automates the analysis of RF signatures.
In GNSS systems:
By combining deep learning with adaptive beamforming, our solutions deliver robust geolocation under jamming conditions — filtering out interfering signals and reinforcing useful signal paths, all in real time.
In critical telecommunications:
Our multi-antenna processing improves link stability in dense or mobile networks, while optimizing dynamic radio resource allocation.
Toward Truly Autonomous Systems
Embedded deep learning fundamentally transforms the way a system operates. By integrating artificial intelligence directly into a radar or GNSS device, we give it the ability to:
- Detect changes in its environment
- Analyse them locally
- Adapt instantly
This operational autonomy is a decisive advantage in aeronautics, defense, space, and surveillance applications.
Go Further
To learn more about the four pillars of our solution, visit the ARKANE Technology page.
