Dimension-reduced STAP Algorithm In Airborne MIMO Radar

Multiple-input multiple-output (MIMO) radar is a new radar technique developed recently, also one of the main directions for the next generation radar. It has gained increasing interest. Because of the application of spatial diversity and signal diversity techniques, the target detection performance of MIMO radar is significantly different from the conventional phased array radar.This thesis focuses on full space-time adaptive processing (STAP) for airborne MIMO radar systems. STAP on the traditional phased array radar has been developed well, and is an important technique for suppressing ground clutter and improving moving target detection performance on airborne radar systems operating in strong ground clutter environments. However, in the airborne MIMO radar system, the dimension of the received spacial and temporal data will be very high. Although, full STAP allows us to filter out more ground clutter with little loss on signal-to-clutter-plus-noise ratio, the very high dimensional processing takes the very large computational cost and data samples. This problem has motivated the development of suboptimal dimension-reduced STAP algorithms in the airborne MIMO radar. Firstly, based on the signal model in airborne MIMO radar, the concept and the characteristic of the MIMO radar will be briefly reviewed. Then, the theory of the STAP, and the ful STAP algorithms in the traditional phased array radar and MIMO radar are followed. Lastly, the suboptimal dimension-reduced STAP algorithm in the airborne MIMO radar will be researched particularly, and the factors which affect the performance of ground clutter suppression are analysed briefly.