2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPTM-8.2
Paper Title A TYLER-TYPE ESTIMATOR OF LOCATION AND SCATTER LEVERAGING RIEMANNIAN OPTIMIZATION
Authors Antoine Collas, Florent Bouchard, CentraleSupélec, Université Paris-Saclay, France; Arnaud Breloy, Université Paris Nanterre, France; Chengfang Ren, CentraleSupélec, Université Paris-Saclay, France; Guillaume Ginolhac, Université Savoie Mont Blanc, France; Jean-Philippe Ovarlez, CentraleSupélec/ONERA, Université Paris-Saclay, France
SessionSPTM-8: Estimation Theory and Methods 2
LocationGather.Town
Session Time:Wednesday, 09 June, 13:00 - 13:45
Presentation Time:Wednesday, 09 June, 13:00 - 13:45
Presentation Poster
Topic Signal Processing Theory and Methods: [SSP] Statistical Signal Processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract 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.