Adaptive Space-Time Beamforming in Radar Systems

1.1 Introduction Space-time adaptive processing (STAP) techniques [Klemm (2002)],[Melvin (2004)] have been thoroughly investigated in the last decades as a key enabling technology for advanced airborne radar applications following the seminal work by Brennan and Reed [Brennan and Reed (1973)]. A great deal of attention has been given to STAP algorithms and different strategies to design space-time beamformers to mitigate the effect of clutter and jamming signals [Reed et al. (1974)]-[Guerci (2000)]. It is well understood that STAP techniques can improve slow-moving target detection through better mainlobe clutter suppression, provide better detection in combined clutter and jamming environments, and offer a significant increase in output signal to-interference-plus-noise-ratio (SINR). Moreover, it is also well understood that clutter and jamming signals often reside in a low-rank signal subspace, which is typically much lower than the number of degrees of freedom of the array and the associated space-time beamformer. Due to the large computational complexity of the matrix inversion operation, the optimum STAP processor is prohibitive for practical implementation. In addition, another very challenging issue that is encountered by the optimal STAP technique is when the number of elements M in the spatio-temporal beamformer is large. It is well known that K ≥ 2M independent and identically distributed (i.i.d) training samples are required for the beamformer to achieve the steady-state performance [Haykin (2002)]. Thus, in dynamic scenarios the optimal STAP with large M usually fails or provides poor performance in tracking target signals contaminated by interference and noise. World Scientific Book-9in x 6in arxiv 2 My Book Title In the recent years, a number of innovative space-time beam-forming algorithms have been reported in the literature for clutter and interference mitigation in radar systems. These algorithms include low-rank and reduced-dimension techniques [Haimovich (1991)]-[de Lamare and Sampaio-Neto (2009)], which employ a two-stage processing framework to exploit the low-rank property of the clutter and the jamming signals. The first stage performs dimensionality reduction and is followed by a second stage that employs a beamforming algorithm with a reduced dimensional filter. Another class of important space-time beam-forming algorithms adopt the strategy of compressive sensing and sparsity-aware algorithms, which exploit the fact that space-time beamformers do not need all their degrees of freedom to mitigate clutter and jamming signals. These algorithms compute sparse space-time beamformers which can converge faster and are effective for STAP in radar systems. By exploiting the low-rank properties of the interference and devising sparse …