A unified framework for M-estimation based robust Kalman smoothing

Hongwei Wang, Hongbin Li, Wei Zhang, Junyi Zuo, Heping Wang

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

We consider the robust smoothing problem for a state-space model with outliers in measurements. A unified framework for robust smoothing based on M-estimation is developed, in which the robust smoothing problem is formulated by replacing the quadratic loss for measurement fitting in the conventional Kalman smoother by a robust cost function from robust statistics. The majorization-minimization method is employed to iteratively solve the formulated robust smoothing problem. In each iteration, a surrogate function is constructed for the robust cost, which enables the states update procedure to be implemented in a similar way as that in a conventional Kalman smoother with a reweighted measurement covariance. Numerical experiments show that the proposed robust approach outperforms the traditional Kalman smoother and several robust filtering methods.

Original languageEnglish
Pages (from-to)61-65
Number of pages5
JournalSignal Processing
Volume158
DOIs
StatePublished - May 2019

Keywords

  • M-estimation
  • Majorization-minimization
  • Robust Kalman smoother
  • State-space modeling

Fingerprint

Dive into the research topics of 'A unified framework for M-estimation based robust Kalman smoothing'. Together they form a unique fingerprint.

Cite this