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 and analytically useful form of diagonally loaded SMI method, is derived here. It covers from Hung-Turner (1983) projection (HTP) algorithm to the matched filter according to the loading factor. Also the optimum loading factor which gives the maximum signal-to-interference-plus-noise ratio (SINR) is derived from the viewpoint of singular value decomposition of the covariance matrix. The simulation results with synthetic data show that the maximum SINR indeed coincides with the proposed optimum loading factor in various data sample situations.