An analysis of the effects of windowing on selected STAP algorithms

Abstract

The paper analyzes the effects of common data windows on STAP algorithms and the non-adaptive signal match processor. The windows are applied both to temporal and spatial dimensions. With the exception of factored approaches, it is shown that STAP algorithm performance decreases when windows are applied. Finally, a Monte Carlo analysis of probability of detection is performed on the best windowed/nonwindowed combination from each technique evaluated. Because the covariance must be estimated, the results demonstrate that the windowed non-adaptive signal match processor can outperform partially adaptive STAP methods at normalized Doppler between 0.25 and 0.75, while partially adaptive STAP algorithms perform significantly better than the windowed signal match processor closer to the clutter normalized Doppler.