Riemannian covariance estimation (riecovest)

This is a package for estimation of signal covariance matrices from noisy signal and noise-only data. The package is using pymanopt to perform optimization over the specified Riemannian manifolds.

API Reference

`riecovest.distance`	Functions for computing distances between matrices.
`riecovest.matrix_operations`	Functions for matrix operations using jax.
`riecovest.covariance_estimation`	Covariance estimation on Riemannian manifolds.
`riecovest.random_matrices`	Functions for randomly sampling vectors and positive matrices.

License

The software is distributed under the MIT license. See the LICENSE file for more information.

The package was developed in the course of the following research. Please consider citing the following papers if relevant to your work.

Robust signal and noise covariance matrix estimation using Riemannian optimization, J. Brunnström, M. Moonen, and F. Elvander

@inproceedings{brunnstromRobust2024,
title = {Robust Signal and Noise Covariance Matrix Estimation Using {{Riemannian}} Optimization},
booktitle = {European {{Signal Processing Conference}} ({{EUSIPCO}})},
author = {Brunnstr{\"o}m, Jesper and Moonen, Marc and Elvander, Filip},
year = {2024},
month = sep,
pages={291-295},
keywords={Manifolds;Heavily-tailed distribution;Noise;Estimation;Europe;Euclidean distance;Cost function;Eigenvalues and eigenfunctions;Covariance matrices;Synthetic data;noise reduction;robust covariance matrix estimation;Riemannian optimization;Hermitian positive matrices;manifolds},
doi={10.23919/EUSIPCO63174.2024.10715247}}
}

Acknowledgements

The software has been developed during a PhD project as part of the SOUNDS ETN project at KU Leuven. The SOUNDS project has recieved funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 956369.