Reduced-rank space-time adaptive processing of measured data
Chongzhao Han · Systems engineering and electronics · 2005
The limitations of the total space-time adaptive processing(STAP) are estimation of interference covariance matrix and substantial computational burden.Aimed at these problems,two reduced-rank space-time adaptive processing methods based on the direct form processes,i.e.eigen-based principal component and cross-spectral metric are introduced.These two approaches are both aimed at the low-rank nature of clutter and jamming observations,and the reduced-dimension transformation applied to the data is necessarily data dependent.The Mountain Top program is described in detail and the measured data are utilized with the Mountain Top program to conduct a comparative analysis of the two reduced-rank STAP techniques.The analysis result shows that these two approaches are effective to reduce computational burden and improve the convergence measure of effectiveness.