New Low-Rank Filters for MIMO-STAP based on an Orthogonal Tensorial Decomposition
Résumé
We develop in this paper a new adaptive LR filter for MIMO-STAP application based on a tensorial modelling of the data. This filter is based on an extension of the HOSVD (which is also one possible extension of SVD to the tensor case), called AU-HOSVD, which allows to consider the combinations of dimensions. This property is necessary to keep the advantages of the STAP and the MIMO characteristics of the data. We show that the choice of a good partition (as well as the tensorial modelling) is not heuristic but have to follow several features. Thanks to the derivation of the theoretical formulation of multimode ranks for all partitions, the tensorial LR filters are easy to compute. Results on simulated data show the good performance of the AU-HOSVD LR filters in terms of secondary data and clutter notch.
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