Fast Iterative Subspace Algorithms for Airborne STAP Radar
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Fast Iterative Subspace Algorithms for Airborne STAP Radar Hocine Belkacemi and Sylvie Marcos Laboratoire des Signaux et Syst`emes (LSS), CNRS, Sup´elec, 3 rue Joliot-Curie, Plateau du Moulon, Gif-sur-Yvette Cedex 91192, France Received 16 December 2005; Revised 30 May 2006; Accepted 16 July 2006 Space-time adaptive processing (STAP) is a crucial technique for the new generation airborne radar for Doppler spread compensation caused by the platform motion. We here propose to apply range cell snapshots-based recursive algorithms in order to reduce the computational complexity of the conventional STAP algorithms and to deal with a possible nonhomogeneity of the data samples. Subspace tracking algorithms as PAST, PASTd, OPAST, and more recently the fast approximate power iteration (FAPI) algorithm, which are time-based recursive algorithms initially introduced in spectral analysis, array processing, are good candidates. In this paper, we more precisely investigate the performance of FAPI for interference suppression in STAP radar. Extensive simulations demonstrate the outperformance of FAPI algorithm over other subspace trackers of similar computational complexity. We demonstrate also its effectiveness using measured data from the multichannel radar measurements (MCARM) program. Copyright © 2006 H. Belkacemi and S. Marcos. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
Space-time adaptive processing (STAP) is a technique for suppressing clutter and jamming in airborne radar [1]. Employing an adaptive array antenna (spatial dimension) and a coherent (pulse) processing interval (CPI), the joint spatiotemporal domain optimization can provide far superior interference mitigation compared to the classical moving target indicator (MTI) methods [2]. In the optimum processor, the weight vector which maximizes the signal-tointerference-plus-noise ratio (SINR) is given by wopt = κR−1 s, where R is the covariance matrix of the interferences and κ a constant gain. Since the covariance matrix is not known, Brennan and Reed [3] proposed the sample matrix inversion (SMI) based on replacing R by the sample aver . In general, there are two computational criteage estimate R ria that a practical implementation should ideally possess to achieve sufficient interference suppression: a rapid convergence (i.e., sample support size) to reduce nonhomogenous samples that contribute for the interference covariance estimation and a low computational complexity for real-time processing. Thus the SMI is a poor technique for the weight computation because it converges slowly requiring a widesense stationary (WSS) sample support of K = 2NM samples to obtain an SINR performance within 3 dB of the optimal one in the Gaussian case, with a computational load of O((NM)3 ). The STAP interference covariance matrix is in general of low-rank. Subspace techniques exploit the low
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