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

The goal of this project is to optimize interference rejection algorithms for real arrays on airborne platforms operating in realistic interference environments. This requires addressing (1) mutual coupling in the array, (2) near-field scattering from the airborne platform, and (3) the non-homogeneity of real-world clutter including large discrete scatterers. These topics have not been effectively addressed in previous algorithm development. The objective of adaptive processing is to weight the received space-time data vectors to maximize the output signal-to-interference plus noise ratio (SINR). Traditionally, the weights are determined based on an estimated covariance matrix of the interference. The weights maximize the gain in the look direction, while placing pattern nulls in the interference directions. This interference plus noise is a combination of clutter, ECM and thermal noise. In Phase I we demonstrated that an integrated electromagnetic modeling/adaptive processing approach was required to effectively address all of the issues associated with real arrays in realistic environments. This integrated approach uses the WIPL-D code for the EM modeling and various covariance matrix and direct data domain algorithms for interference rejection. The best interference rejection that can be obtained is a function of the antenna concept, the target Doppler and of the interference environment.