Subspace-based and single dataset methods for STAP in heterogeneous environments
Résumé
Heterogeneous situations are a serious problem for Space- Time Adaptive Processing (STAP) in an airborne radar context. Indeed, traditional STAP detectors need secondary training data that have to be target free and homogeneous with the tested data. Hence the performances of these detectors are severely impacted when facing a heavily heterogeneous environment. Single dataset algorithms such as APES have proved their efficiency to overcome this problem by only using primary data. However, restricting the estimation domain to the sole primary data often implies a bad estimation of the covariance matrix which can cause a performance degradation. We here investigate the use of reduced-rank STAP on the single dataset APES method.
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