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

Technical Program

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
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.