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Fast Iterative algorithms for Airborne Radar Processing

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, the data are not stationary. Consequently, to circumvent such a problem, we propose to investigate the performances of adaptive recursive subspacebased algorithms of linear complexity using projection approximation subspace tracking (PAST) and orthonormal PAST(OPAST). In addition, we apply the fast implementation of the power iterations method for subspace tracking (FAPI), based on a less restrictive approximation than the well known projection approximation (API). Performance curves show that PAST, OPAST, API and FAPI algorithms do indeed allow a good detection of slow moving targets even with a low rank covariance matrix. We also show that in the case of Doppler ambiguous environment when combined a pseudo random staggered PRF, these algorithms give better results than the methods based on eigenvalues decomposition. Keywords-Airborne Radar Processing; STAP.