Research map / Learning theory and algorithms / Prediction and decision losses
Wasserstein Distributionally Robust Kalman Filtering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the Wasserstein robust MMSE estimator instead of the nominal Bayesian MMSE estimator | MMSE estimation · Synthetic Gaussian estimation (d in {10, 50, 100}) | mean square error under the true distribution | Helped |
| Use a Wasserstein distributionally robust filter instead of the classical Kalman filter | Kalman filtering · Small time-invariant uncertainty | steady-state mean square error | Helped |
| Use a KL distributionally robust filter instead of the classical Kalman filter | Kalman filtering · Small time-invariant uncertainty | steady-state mean square error | Helped |
| Use a distributionally robust filter instead of the classical Kalman filter | Kalman filtering · Small time-varying uncertainty | mean square error | No clear effect |
| Use a Wasserstein distributionally robust filter instead of the classical Kalman filter | Kalman filtering · Large time-invariant uncertainty | steady-state mean square error | Helped |
| Use a Wasserstein distributionally robust filter instead of a KL distributionally robust filter | Kalman filtering · Large time-invariant uncertainty | steady-state mean square error | Helped |
| Use a Wasserstein distributionally robust filter instead of the classical Kalman filter | Kalman filtering · Large time-varying uncertainty | steady-state mean square error | Helped |
| Use a Wasserstein distributionally robust filter instead of a KL distributionally robust filter | Kalman filtering · Large time-varying uncertainty | steady-state mean square error | Helped |
| Use the Wasserstein distributionally robust filter | Kalman filtering · Large uncertainty scenarios | transient mean square error | Helped |
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