Journal
IEEE SIGNAL PROCESSING LETTERS
Volume 25, Issue 8, Pages 1236-1240Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2018.2851156
Keywords
beta-Bernoulli distribution; outlier detection; Robust Kalman filtering; state-space modeling
Categories
Funding
- China Scholarship Council
- National Natural Science Foundation of China [61473227, 11472222, U1530154]
- National Science Foundation [ECCS-1609393]
- Air Force Office of Scientific Research [FA9550-16-1-0243]
Ask authors/readers for more resources
We consider the nonlinear robust filtering problem where the measurements are partially disturbed by outliers. A new robust Kalman filter based on a detect-and-reject idea is developed. To identify and exclude outliers automatically, each measurement is assigned an indicator variable, which is modeled by a beta-Bernoulli prior. The mean-field variational Bayesian method is then utilized to estimate the state of interest as well as the indicator in an iterative manner at each time instant. Simulation results reveal that the proposed algorithm outperforms several recent robust solutions with higher computational efficiency and better accuracy.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available