Details

Autor(en) / Beteiligte

• Collas, A.
• Bouchard, F.
• Breloy, A.
• Ren, C.
• Ginolhac, G.
• Ovarlez, J.-P.

Titel

A Tyler-Type Estimator of Location and Scatter Leveraging Riemannian Optimization

Ist Teil von

• ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, p.5160-5164

Ort / Verlag

IEEE

Erscheinungsjahr

2021

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• We consider the problem of jointly estimating the location and scatter matrix of a Compound Gaussian distribution with unknown deterministic texture parameters. When the location is known, the Maximum Likelihood Estimator (MLE) of the scatter matrix corresponds to Tyler's M-estimator, which can be computed using fixed point iterations. However, when the location is unknown, the joint estimation problem remains challenging since the associated standard fixed-point procedure to evaluate the solution may often diverge. In this paper, we propose a stable algorithm based on Riemannian optimization for this problem. Finally, numerical simulations show the good performance and usefulness of the proposed algorithm.