A Riemannian approach for training data selection in space-time adaptive processing applications

Abstract : Heterogeneous situations are a serious problem for Space-Time Adaptive Processing (STAP) in an airborne radar context. Indeed, STAP detectors need secondary training data that have to be homogeneous with the tested data, otherwise the performances of these detectors are severely impacted when facing heterogeneous environments. Hence, training data have to be carefully selected and this is traditionally done in Euclidean geometry. We introduce a new criterion for data selection. We show that it can be viewed as an approximation of the metric distance in Riemannian geometry.
Complete list of metadatas

https://hal-supelec.archives-ouvertes.fr/hal-00933419
Contributor : Sylvie Marcos <>
Submitted on : Monday, January 20, 2014 - 2:52:20 PM
Last modification on : Tuesday, March 26, 2019 - 2:24:45 PM

Identifiers

  • HAL Id : hal-00933419, version 1

Collections

Citation

Jean-François Degurse, Laurent Savy, Sylvie Marcos, Jean-Philippe Molinié. A Riemannian approach for training data selection in space-time adaptive processing applications. IRS 2013, Jun 2013, Dresden, Germany. pp.319 - 324. ⟨hal-00933419⟩

Share

Metrics

Record views

215