A Novel Adaptive Kalman Filter With Inaccurate Process and Measurement Noise Covariance Matrices

In this paper, a novel variational Bayesian (VB)-based adaptive Kalman filter (VBAKF) for linear Gaussian state-space models with inaccurate process and measurement noise covariance matrices is proposed. By choosing inverse Wishart priors, the state together with the predicted error and measurement...

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Veröffentlicht in:	IEEE transactions on automatic control 2018-02, Vol.63 (2), p.594-601
Hauptverfasser:	Huang, Yulong, Zhang, Yonggang, Wu, Zhemin, Li, Ning, Chambers, Jonathon
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptive filtering Bayes methods Covariance matrices Estimation error inverse Wishart distribution Kalman filtering Kalman filters Noise measurement State-space methods Target tracking time-varying noise covariance matrices variational Bayesian (VB)
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