Laboratory Risk Aware Model-Based Learning Automation for EMC, La Rambla-EMC

Project objectives and tasks

The main objective of the La RAMBLA for EMC project is to develop an automated, data-driven framework for electromagnetic compatibility (EMC) risk management in large and complex configurable systems. Electromagnetic compatibility ensures that electronic and electrical equipment can operate reliably without causing or being affected by unwanted electromagnetic interference. Demonstrating compliance with EMC requirements is becoming increasingly challenging, particularly for safety-critical systems consisting of multiple interconnected components. Although many EMC standards require manufacturers to perform risk assessments to determine appropriate performance criteria and testing levels, manufacturers, laboratories, certification bodies and regulators continue to face difficulties in applying these requirements consistently and efficiently. The project aims to address these challenges by combining systems engineering, structured risk management, automation and artificial intelligence (AI) and machine learning (ML) technologies to improve EMC assessment, reduce testing time and support the development of safer and more reliable systems.

The project will focus on developing a systematic approach to EMC risk management and automating the preparation, execution and analysis of representative EMC tests. The main tasks include establishing risk management processes for safety-critical systems of systems; developing systems engineering models incorporating EMC-related information, classifications and relationships; creating relational and time-series data models for EMC data; and developing AI/ML-based EMC test recipe builders within the La RAMBLA platform using relevant application domains and usage profiles. Further activities will address EMC simulation and test automation, AI/ML-supported data analysis and automated report generation. The expected results include a more efficient and consistent approach to EMC compliance assessment, reduced measurement and testing time, improved use of test data, better-informed risk-based decisions and enhanced electromagnetic resilience, safety and reliability of complex systems. The project will also strengthen collaboration across safety-critical industries and value chains by enabling knowledge sharing and supporting the development of innovative EMC-resilient system design and testing practices.

EDI's tasks and role in the project

The Institute of Electronics and Computer Science (EDI) contributes to the La RAMBLA for EMC project by providing expertise in radar (RADAR) technology, electromagnetic interference (EMI) analysis, simulation-based design and artificial intelligence (AI). EDI’s role focuses on investigating how electromagnetic interference affects radar system performance and developing methods and tools to support the design of more robust and reliable radar systems. EDI will contribute to defining the technical collaboration environment and the architecture of the La RAMBLA framework, with particular emphasis on simulation-based automation of radar design while accounting for electromagnetic interference. EDI will analyse and compare existing commercial and non-commercial electromagnetic simulation tools to evaluate their suitability for assessing the effects of electromagnetic interference on radar performance. Building on its existing radar technology and digital twin concepts, EDI will develop methodologies and tools for analysing EMI effects on radar systems. This work will help identify ways to improve the design of existing radar solutions and support the development of electromagnetic sensors and communication systems. EDI will also use its radar technology to simulate potentially harmful EMI effects, including, where applicable, interference between radar systems.

EDI will investigate the use of AI in developing the EMC recipe builder to generate design guidelines that improve resistance to electromagnetic interference. This will include developing AI models capable of predicting the effects of EMI on radar performance and integrating these models into digital twin environments to enable intelligent and adaptive system design. EDI will also provide real-world radar data for model training and validation. The expected contributions include improved understanding and prediction of EMI effects, simulation-based methods for evaluating radar resilience, and AI-supported tools and design guidelines that help engineers develop more reliable radar, sensor and communication systems.

EUREKA cluster Xecs call project Nr. 2025001 La Rambla-EMC is supported by Latvian Council of Science under grant Nr. ES RTD/2026/26