| Paper ID | SPTM-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 |
| Session | SPTM-8: Estimation Theory and Methods 2 |
| Location | Gather.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 |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
