Luqi Yang

The main objectives are the design of low complexity and high resolution AI-enhanced dual monostatic – bistatic systems that could be used in mobile scenarios working simultaneously as monostatic and bistatic. To develop the signal processing associated to leverage on the dual working strategy to reduce complexity locally and improve robustness and resolution. To develop intelligent cooperative solutions and interference cancellation algorithms for obtaining better performance than current monostatic or bistatic ISAC systems. To enhance by using Machine Learning (ML) technique the performance and the resolution. To explore the use of ML for managing interference and complex environments. The research anticipates an inflexion point in the design of either monostatic and bistatic ISAC systems with the application of dual signals to avoid disadvantages and pain points of both designs and leverage the advantages of both worlds at the same time. Since complexity is going to be a key parameter, design for light terminals (mobility), some of the experience and results obtained in this research are likely to be very useful for other designs. Robust yet flexible algorithms for dual monostatic-bistatic designs are expected. Their enhanced performance is going to be validated through both theoretical analysis and simulations. Some of them are planned to be implemented in the demonstrator for final results.