Reduced-rank space-time adaptive processing to radar measure data

This paper firstly introduces the correlation dimension non-homogeneity detection, to select the secondary range cell and estimate the correlation matrix. Then respectively discusses reduced-rank STAP based on direct form process (DFP) and generalized sidelobe canceller (GSC). Those approaches all take advantage of the low rank nature of clutter and jamming observations, and the reduced-dimension transformation applied to the data are necessarily data dependent. Lastly uses the Mountain Top measure data to validate these reduced-rand STAP technique. Theory analysis and simulation results all show that those schemes can make the residual power least, and reduce computational burden.