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Proceedings Paper

Riemannian mean and space-time adaptive processing using projection and inversion algorithms
Author(s): Bhashyam Balaji; Frédéric Barbaresco
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Paper Abstract

The estimation of the covariance matrix from real data is required in the application of space-time adaptive processing (STAP) to an airborne ground moving target indication (GMTI) radar. A natural approach to estimation of the covariance matrix that is based on the information geometry has been proposed. In this paper, the output of the Riemannian mean is used in inversion and projection algorithms. It is found that the projection class of algorithms can yield very significant gains, even when the gains due to inversion-based algorithms are marginal over standard algorithms. The performance of the projection class of algorithms does not appear to be overly sensitive to the projected subspace dimension.

Paper Details

Date Published: 31 May 2013
PDF: 8 pages
Proc. SPIE 8714, Radar Sensor Technology XVII, 871419 (31 May 2013); doi: 10.1117/12.2017878
Show Author Affiliations
Bhashyam Balaji, Defence Research and Development Canada, Ottawa (Canada)
Frédéric Barbaresco, Thales Air Systems S.A. (France)


Published in SPIE Proceedings Vol. 8714:
Radar Sensor Technology XVII
Kenneth I. Ranney; Armin Doerry, Editor(s)

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