Improving GNSS Resiliency Using Edge AI Solutions
Improving the resilience of Global Navigation Satellite Systems (GNSS) is crucial, especially in challenging environments where global positioning systems (GPS) signals can be weak or disrupted. Enabling Edge Artificial Intelligence (Edge AI) offers promising solutions that employ AI algorithms and models on edge devices without constant reliance on cloud infrastructures; especially in highly dense blockage environments, is an interesting area of research. Our strategy will explore how Edge AI can enhance GNSS resiliency in challenging scenarios by bringing intelligence to the edge node. In dense urban environments, where reliable measurements are often inaccessible due to obstacles, Edge AI techniques can play a crucial role. When deploying Edge AI in dense blockage environments, such as urban canyons or indoor spaces, we will consider the following: 1. Edge Computing Infrastructure for GNSS Services: Setting up edge servers or devices close to the GNSS receivers to process AI algorithms locally. This minimizes latency and ensures real-time decision-making. Opting for low-power, compact edge devices that can handle AI workloads efficiently. 2. AI Algorithms for Signal Enhancement: Employing machine learning models to enhance GNSS signals affected by multipath reflections, interference, and blockages. Techniques like deep learning, Kalman filtering, and particle filters will be investigated for positioning accuracy enhancement by mitigating noisy measurements. 3. GNSS Abnormalities Detection and Mitigation Create a more robust GNSS system Employe multiple positioning sources for redundancy and come up with the concept of multi- level accuracy support Employe AI for GNSS abnormalities detection, abnormal behavior includes attacks and failures Come up with GNSS abnormalities mitigation techniques 4. Cooperative Learning Allow devices to share positioning information and learn in a cooperative approach using distributed learning This will allow low end devices to benefit from more capable devices with higher accuracy GNSS support 5. Map-Based Localization: Pre-existing maps will be leveraged, or local maps of the environment will be created. These maps can help in predicting GNSS signal blockages and aid in localization. Techniques like SLAM (Simultaneous Localization and Mapping) will be considered. 6. Dynamic Adaptation and Hybrid Positioning (Coexistence Networking Strategies (CNS): Implementing adaptive algorithms that adjust parameters based on real-time conditions. Hybrid positioning approaches provide redundancies and improve availability in challenging environments. For example, if a GNSS signal is blocked due to tall buildings, the system will be complemented by additional technologies such as Wi-Fi or Bluetooth for localization. This project will employ Edge AI solutions that are tailored to the challenging environments and use cases. Regular testing and validation will be implemented to ensure reliable performance.
Language
- English
Project
- Status: Active
- Funding: $Federal $125000, Cost-share $39426
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Contract Numbers:
69A3552348324
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Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
Center for Assured and Resilient Navigation in Advanced Transportation Systems
Illinois Institute of Technology
Chicago, IL United States 60616 -
Project Managers:
Narang, Aashish
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Performing Organizations:
Center for Assured and Resilient Navigation in Advanced Transportation Systems
Illinois Institute of Technology
Chicago, IL United States 60616 -
Principal Investigators:
Ayyash, Moussa
- Start Date: 20241001
- Expected Completion Date: 20250930
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers Program
- Subprogram: University Transportation Centers
Subject/Index Terms
- TRT Terms: Artificial intelligence; Computer security; Disaster resilience
- Identifier Terms: Global navigation satellite systems
- Subject Areas: Data and Information Technology; Highways; Security and Emergencies;
Filing Info
- Accession Number: 01934811
- Record Type: Research project
- Source Agency: Center for Assured and Resilient Navigation in Advanced Transportation Systems
- Contract Numbers: 69A3552348324
- Files: UTC, RIP
- Created Date: Oct 22 2024 4:32PM