To cope with detection mission in complex scenarios, modern phased array radars usually adopt large-scale, multi-functional, and digital-analog hybrid system architecture. The modern system architecture provides a solid "hardware" for various complex detection tasks, but the corresponding beamforming algorithm faces a series of new challenges, i.e., needs to solve large-scale constraints beamforming problems. Combining the latest optimization theory, this project explores primal-dual optimization methods to quickly and effectively solve such complex large-scale constraints problems. The goal is to establish a primal-dual optimization algorithm framework for radar beamforming tasks, and provide basic optimization theory and key optimization technique support for radar beamforming technology.

In the past, research on beamforming technology was oriented to application scenarios and task requirements, and most of the research work was carried out from the perspective of signal processing. Although many optimization methods have been proposed and used to solve specific beamforming problems. However, the common optimization methods for beamforming applications need to be further studied, especially under large-scale and complex constraints.

Key contribution/commercial implication

The optimization technology studied has a strong pertinence to the challenges faced in the field of beamforming. This project provides key optimization technical support for various radar beamforming problems. 

Next steps

Improve optimization technique for different beamforming scenarios.

Team/contributors

Wenqiang PU