Generalized forward/backward subaperture smoothing techniques for sample starved STAP

A major issue in space-time adaptive processing (STAP) for moving target indicator (MTI) radar is the so-called sample support problem. Often, the available sample support for estimating the requisite interference covariance matrix is inadequate, thereby precluding STAP beamforming utilizing many adaptive degrees-of-freedom (DOFs). Although deterministic rank-reduction methods can reduce sample support requirements, they are invariably suboptimal from a signal-to-interference-plus-noise-ratio (SINR) standpoint. In this paper, a new generalized subspatial and subtemporal aperture smoothing method employing forward and backward data vectors is introduced to overcome the data deficiency problem. It is shown that multiplicative improvement in data samples can be obtained at the expense of negligible loss in space-time aperture of the steering vector.