Space-time adaptive array processing has emerged as a key technology thrust area for the next generation of airborne radar systems due t o its inherent potential for vastly improving moving target indicator (MTI) performance. Unfortunately, these performance gains come with a commensurate increase in on-line computational complexity if full degree-of-freedom (DOF) STAP processors are employed. In this paper we introduce a class of efficient STAP processors which exploit the fact that the full DOF space-time clutter covariance matrix is rank deficient with respect to a coherent processing interval (CPI).

A major issue in space-time adaptive processing (STALP) for airborne moving target indicator (MTI) radar is the so-called sample support problem. Often, the available sample support for estimating the interference covariance matrix leads to severe rank deficiency, thereby precluding STAP beamforming based on the direct sample matrix inversion (SMI) method. The intrinsic mterference subspace removal (ISR) technique, which is a computationally useful form of diagonally loaded SIMI method, can handle this case, although the performance is poor in low sample situations. In this context, new subarray-subpulse schemes using forward and backward data vectors are introduced to overcome the data deficiency problem. It is shown here that multiplicative improvement in data samples can be obtained at the expense of negligible loss in space-time aperture of the steering vector.