riecovest.covariance_estimation.est_manifold_wishart

riecovest.covariance_estimation.est_manifold_wishart(scm_noisy_signal, scm_noise, rank, num_scm_samples, alpha=0.5, start_value=None)

Signal and noise covariance estimation using the Wishart log-likelihood distance.

Parameters:

  • scm_noisy_signal (ndarray of shape (ambient_dim, ambient_dim)) – Sample covariance matrix of the noisy signal.

  • scm_noise (ndarray of shape (ambient_dim, ambient_dim)) – Sample covariance matrix of the noise.

  • rank (int) – Rank of the signal covariance matrix.

  • num_scm_samples (int) – Number of samples used to estimate the sample covariance matrix. Defines the degrees of freedom of the Wishart distribution.

  • alpha (float, optional) – Weighting parameter between the noisy signal and the noise. Default is 0.5.

  • start_value (tuple of ndarrays, optional) – Initial value for the optimization. If None, the optimization starts from a random point.

Returns:

  • Rx (ndarray of shape (ambient_dim, ambient_dim)) – Estimated signal covariance matrix.

  • Rv (ndarray of shape (ambient_dim, ambient_dim)) – Estimated noise covariance matrix.