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EUROPEAN SPACE RESEARCH AND TECHNOLOGY CENTRE (ESTEC) has floated a tender for Verifiable Ai/Ml Techniques for PNT Applications - Expro Plus. The project location is Netherlands and the tender is closing on 23 Feb 2024. The tender notice number is NAVISP-EL1-087, while the TOT Ref Number is 94727341. Bidders can have further information about the Tender and can request the complete Tender document by Registering on the site.
Procurement Summary
Country : Netherlands
Summary : Verifiable Ai/Ml Techniques for PNT Applications - Expro Plus
Deadline : 23 Feb 2024
Other Information
Notice Type : Tender
TOT Ref.No.: 94727341
Document Ref. No. : NAVISP-EL1-087
Competition : ICB
Financier : Agency for the Cooperation of Energy Regulators (ACER)
Purchaser Ownership : Public
Tender Value : Refer Document
Purchaser's Detail
Purchaser : EUROPEAN SPACE RESEARCH AND TECHNOLOGY CENTRE (ESTEC)
Keplerlaan 1 Postbus 299 2200 AG Noordwijk (The Netherlands)
Tel: +31-71 565 6565
Netherlands
Tender Details
Tenders are invited for Navisp-el1-087 Verifiable Ai/Ml Techniques for Pnt Applications - Expro Plus
Artificial intelligence (AI) is increasingly utilized in the PNT field, enhancing the performance and reliability of Global Navigation Satellite Systems (GNSS), from RF interference detection and mitigation to sensor fusion. The advancement and ever-increasing size of neural networks increase the complexity of applications supported by AI, and with the increasing complexity, verifiability decreases. Unreliable or biased AI systems can have serious consequences, risking damage both to the environment and to human lives, especially in domains like autonomous vehicles, UAVs, and marine applications.To overcome concerns about data biases, insufficient data, and lack of transparency, effective techniques such as data augmentation, transfer learning, and Explainable AI (XAI) methods can be employed. However, addressing issues like inaccurate specifications and algorithmic biases is even more crucial to avoid incorrect and undesirable outcomes. Therefore, it is essential to develop methods that can verify the behaviour of AI systems.Verified AI, as defined by the Association for Computing Machinery (ACM), seeks to design AI systems that provide robust assurance, ideally with provable correctness based on mathematically specified requirements. This approach is crucial to ensure reliable and trustworthy operation of AI systems in PNT applications
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