A major issue in space-time adaptive processing (STAP) 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 interference subspace removal (ISR) technique, which is a computationally useful form of diagonally loaded SMI 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 that a multiplicative improvement in data samples can be obtained at the expense of negligible loss in space-time aperture of the steering vector.