Knowledge-aided space-time adaptive processing based on Morchin model

This paper mainly deals with the performance degradation for space-time adaptive processing in rugged circumstances resulted from insufficiency of applying priori knowledge, and an improved knowledge-aided algorithm is proposed based on Morchin clutter model. In this algorithm, the grazing angle, terrain classification, terrain masking of all clutter cells is estimated through digital terrain information and digital elevation data considered as priori knowledge and, in turn, the radar blind region caused by terrain masking is distinguished to be removed. Meanwhile, the Morchin model is applied to incorporate all the information acquired above to calculate the estimate of back-scattering coefficient, through which the secondary data is selected to estimate the clutter covariance matrix. The simulation results evaluate the algorithm performance and confirm the validity of proposed KA-STAP algorithm based on the Morchin clutter model.