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)’, that inherits the superiority of signal and navigation processing through Deep learning technology. To achieve reconfigurability and optimized performance, Graphics processing unit (GPU) based Software Defined Radio (SDR) approach is preferred for JASMINE.
