A novel clutter suppression algorithm with Kalman filtering

Reduced-dimension space-time adaptive processing (STAP) techniques are good choices in multichannel wide-area surveillance airborne systems to suppress ground clutter. However, their performance often degrades in nonhomogeneous environments due to the inaccurate estimation of the interference covariance matrix from the secondary data. In this paper, combing with Kalman filtering, we propose a novel algorithm to suppress ground clutter based on the data model of radar echoes from multichannel wide-area surveillance systems. Since the proposed algorithm does not need to estimate the interference covariance matrix, it has a big advantage when processing the data from nonhomogeneous environments. The effectiveness of the proposed algorithm is validated by the simulated data from PAMIR system.