期刊
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
卷 49, 期 10, 页码 2082-2096出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2017.2778269
关键词
Gaussian scale mixture (GSM) distribution; heavy-tailed noise; Kalman filter; skewed noise; state estimation; target tracking; variational Bayesian (VB)
资金
- National Natural Science Foundation of China [61773133, 61633008, 61773131, U1509217]
- Natural Science Foundation of Heilongjiang Province [F2016008]
- Fundamental Research Founds for the Central University of Harbin Engineering University [HEUCFP201705, HEUCF041702]
- Ph.D. Student Research and Innovation Fund of the Fundamental Research Founds for the Central Universities [HEUGIP201706]
- China Scholarship Council Foundation
- Engineering and Physical Sciences Research Council of the U.K. [EP/K014307/1]
- Australian Research Council [DP170102644]
- 111 Project [B17048, B17017]
- EPSRC [EP/K014307/2] Funding Source: UKRI
In this paper, a new robust Kalman filtering framework for a linear system with non-Gaussian heavy-tailed and/or skewed state and measurement noises is proposed through modeling one-step prediction and likelihood probability density functions as Gaussian scale mixture (GSM) distributions. The state vector, mixing parameters, scale matrices, and shape parameters arc simultaneously inferred utilizing standard variational Bayesian approach. As the implementations of the proposed method, several solutions corresponding to some special GSM distributions are derived. The proposed robust Kalman filters are tested in a manoeuvring target tracking example. Simulation results show that the proposed robust Kalman filters have a better estimation accuracy and smaller biases compared to the existing state-of-the-art Kalman filters.
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