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


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.

In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Arnaud Breloy
Arnaud Breloy
Associate professor (MCF-HDR)