An Adaptive Covariance Parameterization Technique for the Ensemble Gaussian Mixture Filter

Abstract

SIAM Journal on Scientific Computing, Volume 46, Issue 3, Page A1949-A1971, June 2024.
Abstract. The ensemble Gaussian mixture filter (EnGMF) combines the simplicity and power of Gaussian mixture models with the provable convergence and power of particle filters. The quality of the EnGMF heavily depends on the choice of covariance matrix in each Gaussian mixture. This work extends the EnGMF to an adaptive choice of covariance based on the parameterized estimates of the sample covariance matrix. Through the use of the expectation maximization algorithm, optimal choices of the covariance matrix parameters are computed in an online fashion. Numerical experiments on the Lorenz ’63 equations show that the proposed methodology converges to classical results known in particle filtering. Further numerical results with more advanced choices of covariance parameterization and the medium-size Lorenz ’96 equations show that the proposed approach can perform significantly better than the standard EnGMF and other classical data assimilation algorithms.