SBIR-STTR Award

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.

Keywords:
Stap Algorithms, Airborne Radar, Adaptive Antenna, Knowledge-Based, Clutter Suppression

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.

Keywords:
Interference Rejection, Lookdown Radar, Space-Time Adaptive Processing (Stap), Knowledge-Based Signa