Bayesian methodsBayesian / computational

Robust Kalman Filter

The Robust Kalman Filter is an extension of the classical Kalman filter designed to maintain reliable state estimation when observations or process noise depart from the Gaussian assumption — particularly when data contain outliers, heavy-tailed distributions, or gross errors. By replacing or downweighting the standard least-squares update with influence-limited or M-estimation-based corrections, it prevents a single anomalous measurement from distorting the entire state estimate.

Open in MethodMindSoonVideoSoon

Read the full method

Members only

Sign in with a free account to read this section.

Sign in

Sources

  1. Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45. DOI: 10.1115/1.3662552
  2. Huber, P. J. & Ronchetti, E. M. (2011). Robust Statistics (2nd ed.). Wiley. ISBN: 978-0470129906

Related methods

Referenced by

ScholarGateRobust Kalman Filter (Robust Kalman Filter). Retrieved 2026-06-04 from https://scholargate.app/en/bayesian/robust-kalman-filter