Novel Knowledge-Based Space-Time Adaptive Processing (STAP) Techniques

This effort is to develop a novel knowledge-based reduced-rank STAP algorithm that can compensate for failures of individual array channels and compare its performance with the direct data domain (D3) STAP algorithms. Particularly, we will investigate the performance of reduced rank and D3 STAP algorithms for an array of electrically short elements. We believe that the combination of array design and novel STAP algorithms will have the most payoff for practical system implementation. This evaluation will address the practical issues such as near-field scattering, mutual coupling, multipath, and various receiver errors, and be performed using the method of moments code WIPL-D. A new knowledge-based (KB) approach will be developed to improve the performance of above algorithms in the practical conditions such as clutter non-homogeneity, channel mismatch resulting from near-field scattering, mutual coupling, multipath, and various receiver errors, etc., and element/channel failures. The KB processing approach will reconfigure the array for STAP algorithms if some elements fail, and choose the best of several possible STAP algorithms, including the selection of algorithm parameters and secondary data.