STAP with knowledge-aided data pre-whitening

This paper presents a framework for incorporating knowledge sources directly in the space-time beamformer of airborne adaptive radars. The algorithm derivation follows the usual linearly-constrained minimum-variance (LCMV) space-time beamformer with additional constraints based on a model of the clutter covariance matrix that is computed using available knowledge about the operating environment. This technique has the desirable property of reducing sample support requirements by "blending" the information contained in the observed radar data and the a priori knowledge sources. Applications of the technique to both full degree-of-freedom (DoF) and reduced DoF beamformer algorithms are considered. The performance of the knowledge-aided beamforming techniques are demonstrated using high-fidelity X-band radar simulation data.