Improved clutter mitigation performance using knowledge-aided space-time adaptive processing

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 beam forming techniques are demonstrated using high-fidelity simulated X-band radar data     

Performance bounds for hot and cold clutter mitigation

A new method is presented for describing the theoretical interference space-time covariance matrix that will be observed in an adaptive airborne radar system under specific topographical conditions. Both hot clutter that is induced by interfering sources and cold clutter that results from the radar transmitter are considered. This method incorporates phenomenology observed under site specific conditions as well as system effects such as array geometry, receiver filtering, and system bandwidth. Use of this formulation rather than sample data analyses that are generally employed enables one to infer performance bounds for site-specific, and thus generally, heterogeneous terrain that are tighter and therefore more meaningful than the thermal noise floor limit