Summary
The Global Navigation Satellite Systems (GNSS) technology is known for precise positioning and timing capability that is of use in diverse fields of science and technology. The rapid development in this field by various nations in terms of deploying new satellite
systems (GPS, GLONASS, Galileo, COMPASS, IRNSS/NAVIC), new signals in different frequency bands (L1, L2, L5, G1, G2, E1, E5a, E5b, B1, B2, B3, etc.) is changing the trend of GNSS receiver design. Especially, the intrinsic flexibility of software-based receiver design approach is becoming a competitor to even highly developed ASICs. The goal of this project is to develop ‘Jamming and Spoofing Resilient Deep Learning based Software-Defined multi-antenna GNSS Receiver (JASMINE)’. JASMINE is a multi-antenna multi-system dual-band GNSS receiver with autonomous integrity ability. In this project we propose to build a novel design approach for software defined GNSS receiver, combining deep learning (DL) approach with the expert knowledge to replace existing GNSS receiver
algorithms. Novel techniques are proposed for multi-GNSS signal acquisition, denoising, orbit determination (Satellite position estimation), threat detection and mitigation (due to jamming, spoofing and ionosphere) by applying prominent deep neural networks (2D CNN, BiLSTM, RNN/LSTM, 1D CNN) and deep reinforcement learning (actor-critic (RNN/LSTM, 2D CNN), Sarsa, Q-learning, Policy Gradients) methods that add intelligence and give unseen capabilities to JASMINE in comprehending an increasingly complex
environment.
The proposed JASMINE supports all GNSS RF-frequencies (compatible with new signals), inherits the superiority of signal and navigation processing algorithms through Deep learning technology, and thus presents excellence performance. To achieve reconfigurability and optimized performance, Graphics processing unit (GPU) based Software Defined Radio (SDR) approach is preferred for JASMINE.
systems (GPS, GLONASS, Galileo, COMPASS, IRNSS/NAVIC), new signals in different frequency bands (L1, L2, L5, G1, G2, E1, E5a, E5b, B1, B2, B3, etc.) is changing the trend of GNSS receiver design. Especially, the intrinsic flexibility of software-based receiver design approach is becoming a competitor to even highly developed ASICs. The goal of this project is to develop ‘Jamming and Spoofing Resilient Deep Learning based Software-Defined multi-antenna GNSS Receiver (JASMINE)’. JASMINE is a multi-antenna multi-system dual-band GNSS receiver with autonomous integrity ability. In this project we propose to build a novel design approach for software defined GNSS receiver, combining deep learning (DL) approach with the expert knowledge to replace existing GNSS receiver
algorithms. Novel techniques are proposed for multi-GNSS signal acquisition, denoising, orbit determination (Satellite position estimation), threat detection and mitigation (due to jamming, spoofing and ionosphere) by applying prominent deep neural networks (2D CNN, BiLSTM, RNN/LSTM, 1D CNN) and deep reinforcement learning (actor-critic (RNN/LSTM, 2D CNN), Sarsa, Q-learning, Policy Gradients) methods that add intelligence and give unseen capabilities to JASMINE in comprehending an increasingly complex
environment.
The proposed JASMINE supports all GNSS RF-frequencies (compatible with new signals), inherits the superiority of signal and navigation processing algorithms through Deep learning technology, and thus presents excellence performance. To achieve reconfigurability and optimized performance, Graphics processing unit (GPU) based Software Defined Radio (SDR) approach is preferred for JASMINE.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101107050 |
| Start date: | 01-09-2023 |
| End date: | 30-09-2025 |
| Total budget - Public funding: | - 215 534,00 Euro |
Cordis data
Original description
The Global Navigation Satellite Systems (GNSS) technology is known for precise positioning and timing capability that is of use in diverse fields of science and technology. The rapid development in this field by various nations in terms of deploying new satellitesystems (GPS, GLONASS, Galileo, COMPASS, IRNSS/NAVIC), new signals in different frequency bands (L1, L2, L5, G1, G2, E1, E5a, E5b, B1, B2, B3, etc.) is changing the trend of GNSS receiver design. Especially, the intrinsic flexibility of software-based receiver design approach is becoming a competitor to even highly developed ASICs. The goal of this project is to develop ‘Jamming and Spoofing Resilient Deep Learning based Software-Defined multi-antenna GNSS Receiver (JASMINE)’. JASMINE is a multi-antenna multi-system dual-band GNSS receiver with autonomous integrity ability. In this project we propose to build a novel design approach for software defined GNSS receiver, combining deep learning (DL) approach with the expert knowledge to replace existing GNSS receiver
algorithms. Novel techniques are proposed for multi-GNSS signal acquisition, denoising, orbit determination (Satellite position estimation), threat detection and mitigation (due to jamming, spoofing and ionosphere) by applying prominent deep neural networks (2D CNN, BiLSTM, RNN/LSTM, 1D CNN) and deep reinforcement learning (actor-critic (RNN/LSTM, 2D CNN), Sarsa, Q-learning, Policy Gradients) methods that add intelligence and give unseen capabilities to JASMINE in comprehending an increasingly complex
environment.
The proposed JASMINE supports all GNSS RF-frequencies (compatible with new signals), inherits the superiority of signal and navigation processing algorithms through Deep learning technology, and thus presents excellence performance. To achieve reconfigurability and optimized performance, Graphics processing unit (GPU) based Software Defined Radio (SDR) approach is preferred for JASMINE.
Status
SIGNEDCall topic
HORIZON-MSCA-2022-PF-01-01Update Date
31-07-2023
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