PAST and OPAST algorithms for STAP in monostatic airborne radar

In this paper, we investigate the use of two iterative algorithms for the suppression of interferences and thus, the detection of slow targets in monostatic airborne radar. The conventional space-time adaptive processing (STAP) such as the sample matrix inversion (SMI) or the Principal Components (PC) methods are computationally costly and require the estimation of the clutter covariance matrix from secondary data, which are assumed to be independent and identically distributed. However, in monostatic airborne radar, because of the platform motion and the inclination of the array, the data are not stationary. Consequently, to circumvent such a problem, we propose to investigate the performances of adaptive recursive subspace-based algorithms of linear complexity using projection approximation subspace tracking (PAST) and orthonormal PAST (OPAST) algorithms. Simulation results are presented and the performance of STAP is discussed with a comparative study to PC and SINR metric methods justifying the use of those algorithms in radar signal processing. Performance curves show that PAST and OPAST algorithms allow good indeed detection of slow moving targets even with a low rank covariance matrix and in a Doppler ambiguous environment.