Wasserstein bounds for non-linear Gaussian filters
Abstract
Most Kalman filters for non-linear systems, such as the unscented Kalman filter, are based on Gaussian approximations. We use Poincaré inequalities to bound the Wasserstein distance between the true joint distribution of the prediction and measurement and its Gaussian approximation. The bounds can be used to assess the performance of non-linear Gaussian filters and determine those filtering approximations that are most likely to induce error.
Fields of Study & Subjects
Mathematical Analysis and Transform Methods
Publication details
- Journal / source
- IEEE Transactions on Automatic Control
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Volume
- 71
- Issue
- 11
- Pages
- 1–6
- Publication date
- 1 January 2026
- Language
- EN