Skip to Main content Skip to Navigation
Conference papers

Subspace-based and single dataset methods for STAP in heterogeneous environments

Abstract : 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.
Complete list of metadata

Cited literature [12 references]  Display  Hide  Download
Contributor : Sylvie Marcos Connect in order to contact the contributor
Submitted on : Thursday, July 2, 2020 - 12:19:47 PM
Last modification on : Sunday, May 2, 2021 - 3:27:48 AM
Long-term archiving on: : Thursday, September 24, 2020 - 4:05:13 AM


Files produced by the author(s)


  • HAL Id : hal-00776374, version 1


Jean François Degurse, Sylvie Marcos, Laurent Savy. Subspace-based and single dataset methods for STAP in heterogeneous environments. IET RADAR 2012 International Conference on Radar Systems, Oct 2012, Glasgow, United Kingdom. pp.1-6. ⟨hal-00776374⟩



Les métriques sont temporairement indisponibles