Contact Technology focus 2 In aerospace, every on-board communication system must operate under extreme constraints, subject to high criticality levels and stringent certification requirements. Reliability is not optional — every satellite, aircraft, or defense system carries major safety and performance stakes. At ARKANE, we develop embedded AI solutions designed to meet these challenges, built on one core principle: our models must be explainable, verifiable, and trustworthy. Understanding AI to Maintain Control Artificial intelligence opens new possibilities in signal processing and communications optimization. But faced with the risk of “black box” behavior, understanding how a system reaches its decisions is essential to maintaining control over them. In aerospace — where criticality is high and embedded systems operate autonomously — AI must remain explainable, verifiable, and trustworthy.    What Is Trustworthy AI in Aerospace?   Trustworthy AI, applied to aerospace, must meet unique requirements in terms of safety, certification, and robustness under extreme conditions. Reliability and consistency:An embedded AI must deliver stable, predictable results — even under extreme conditions such as jamming, noise, or signal loss. In a radar or communications system, reliability is measured by performance stability: packet delivery rate (PDR), target detection accuracy, trajectory estimation. Security and confidentiality:Aerospace communications handle sensitive data. Trustworthy AI must guarantee the protection of that information and resist cyberattacks, while complying with applicable safety standards. Criticality and certification:In an environment where the slightest failure can compromise a mission, AI must be certifiable. This requires algorithms that are explainable, traceable, and auditable — compliant with aeronautical and space standards such as DO-178C and ECSS. Adaptability and robustness:Radio environments are inherently dynamic — subject to interference, mobility, and jamming. Reliable AI must adapt in real time and remain robust in the face of the unexpected.   How to Build Trustworthy AI: Deep Learning for Communications and Radar Systems In the communications and radar domain, building trustworthy AI means mastering every stage of the lifecycle: data, training, usage scoping, traceability, explainability, and certification. Data quality and diversity:Representative datasets covering a wide range of propagation, jamming, and radar detection scenarios. Rigorous annotation and validation:Ensuring label accuracy — signals, targets, interferences — to produce reliable training data. Bias correction:Identifying and correcting biases to ensure consistent performance in real-world conditions. Usage scoping:A good local AI is not one that can do everything — it is one that does one specific task well. Example: a communications AI can be certified to optimize spectral allocation within a defined frequency range and under specified load scenarios, but not for all unknown situations. Example: a radar deep learning network can be validated for target detection within a specific range of distances and angles, but cannot be extrapolated to untested edge cases.   Traceability and explainability:Ensuring that every decision made by the neural network can be traced and justified. This involves inference logging, analysis of internal weights and activations, and the use of Explainable AI (XAI) methods such as saliency maps, feature attribution, and sensitivity analysis.   Continuous evaluation and certification:Testing models in simulation and under operational conditions to ensure their validity, stability, and compliance with applicable standards.   Robust Systems in Changing Environments Our research is grounded in hybrid signal processing — combining the rigor of classical methods with the power of artificial intelligence. This approach makes it possible to test algorithmic sensitivity and evaluate robustness against constantly evolving conditions. The result: more reliable embedded AI that adapts locally, while giving operators the confidence they need in the bounded autonomous behavior of the system.    Strategic Applications: AI-Powered Radar and Communications for Aerospace Among the most significant applications of our work are intelligent radar systems and aerospace communication systems. Essential for detection, navigation, and communications security, these systems must operate reliably and explainably — including in heavily degraded environments.   In Summary ARKANE develops embedded AI solutions purpose-built for aerospace. Our work rests on concrete technical principles that ensure reliability, robustness, and compliance with the first certification requirements for deep learning models: Local embedded AI, adapted to the critical constraints of aerospace applications Hybrid signal processing, combining classical methods and deep learning — with progressive deep learning integration into traditional signal processing chains for better control over model predictions Rigorous validation on representative datasets covering propagation, jamming, and radar detection scenarios Usage scoping, to guarantee specialized and certifiable models Built-in explainability and traceability, to understand and justify every decision Proven robustness against changing and degraded environments Strategic applications: reliable and safe aerospace communications and radar systems        Go further: To learn more about the four pillars of our solution, visit the ARKANE Technology page.   These articles might interest you 

Contact Technology Focus 1 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. These articles might interest you