New Machine Learning Framework Helps Make Aerospace Platforms Harder to Detect by Radar

Anand Rawat, conducted research that introduces a novel machine learning-assisted optimization framework capable of significantly accelerating the design of radar-stealth systems for aircraft, drones, and other aerospace platforms.
He said "Conventional radar cross-section (RCS) design methods rely on high-fidelity electromagnetic simulations that, while accurate, are computationally expensive and impractical for rapid design iteration. To address this challenge, I developed a hybrid framework that combines machine learning-based surrogate modeling with a genetic algorithm to efficiently minimize the worst-case radar cross-section across all observation angles."

This framework enables engineers and researchers to explore a large number of design alternatives in a fraction of the time required by traditional approaches. By integrating physics-inspired learning models with evolutionary optimization, the method identifies robust low-observable designs without incurring exponential computational cost.

The system was validated in a controlled benchmark environment using simplified synthetic data to train the surrogate model. A Random Forest-based predictor was employed to rapidly evaluate candidate designs, enabling practical and scalable optimization workflows.
Key contributions of this research include:

• Significant computational speed-up:
Machine learning surrogate models dramatically reduce evaluation time compared to full electromagnetic solvers.
• Worst-case-oriented optimization:
Optimizing for worst-case RCS ensures consistent stealth performance across all viewing angles rather than angle-specific solutions.
• Standalone design tool:
The framework is implemented as an integrated graphical software tool with visualization, optimization, and reporting capabilities.

The results highlight the strong potential of combining artificial intelligence with optimization techniques to accelerate early-stage stealth and low-observable system design. While future work will focus on coupling this framework with high-fidelity electromagnetic solvers, the current results establish a solid foundation for rapid and robust stealth design exploration.

CypherInfotech
138A
Shyam Nagar, kanpur
Uttar Pardesh, India
Contact:
Anand Rawat
anandrawat@cypherinfotech.in

Eternity Labs is the advanced R&D engine of CypherInfotech, dedicated to solving critical challenges in defense, biomedical science, and artificial intelligence.

This release was published on openPR.