TY - GEN
T1 - Laplace ℓ1 robust Kalman filter based on majorization minimization
AU - Wang, Hongwei
AU - Li, Hongbin
AU - Zhang, Wei
AU - Wang, Heping
N1 - Publisher Copyright:
© 2017 International Society of Information Fusion (ISIF).
PY - 2017/8/11
Y1 - 2017/8/11
N2 - In this paper, we attack the estimation problem in Kalman filtering when the measurements are contaminated by outliers. We employ the Laplace distribution to model the underlying non-Gaussian measurement process. The maximum posterior estimation is solved by the majorization minimization (MM) approach. This yields an MM based robust filter, where the intractable ℓ1 norm problem is converted into an ℓ2 norm format. Furthermore, we implement the MM based robust filter in the Kalman filtering framework and develop a Laplace ℓ1 robust Kalman filter. The proposed algorithm is tested by numerical simulations. The robustness of our algorithm has been borne out when compared with other robust filters, especially in scenarios of heavy outliers.
AB - In this paper, we attack the estimation problem in Kalman filtering when the measurements are contaminated by outliers. We employ the Laplace distribution to model the underlying non-Gaussian measurement process. The maximum posterior estimation is solved by the majorization minimization (MM) approach. This yields an MM based robust filter, where the intractable ℓ1 norm problem is converted into an ℓ2 norm format. Furthermore, we implement the MM based robust filter in the Kalman filtering framework and develop a Laplace ℓ1 robust Kalman filter. The proposed algorithm is tested by numerical simulations. The robustness of our algorithm has been borne out when compared with other robust filters, especially in scenarios of heavy outliers.
UR - http://www.scopus.com/inward/record.url?scp=85029426227&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029426227&partnerID=8YFLogxK
U2 - 10.23919/ICIF.2017.8009803
DO - 10.23919/ICIF.2017.8009803
M3 - Conference contribution
AN - SCOPUS:85029426227
T3 - 20th International Conference on Information Fusion, Fusion 2017 - Proceedings
BT - 20th International Conference on Information Fusion, Fusion 2017 - Proceedings
T2 - 20th International Conference on Information Fusion, Fusion 2017
Y2 - 10 July 2017 through 13 July 2017
ER -